diff --git "a/-dE3T4oBgHgl3EQfSgnV/content/tmp_files/load_file.txt" "b/-dE3T4oBgHgl3EQfSgnV/content/tmp_files/load_file.txt"
new file mode 100644--- /dev/null
+++ "b/-dE3T4oBgHgl3EQfSgnV/content/tmp_files/load_file.txt"
@@ -0,0 +1,2206 @@
+filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf,len=2205
+page_content='Springer Nature 2021 LATEX template  Multilingual Entity and Relation Extraction  from Unified to Language-specific Training  Zixiang Wang1, Jian Yang1, Tongliang Li1, Jiaheng  Liu1, Ying Mo1, Jiaqi Bai1, Longtao He2 and Zhoujun Li1*  1State Key Lab of Software Development Environment, Beihang  University, Beijing, Beijing, China.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  2National Computer Network Emergency Response Technical  Team/Coordination Center of China, Beijing, Beijing, China.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Corresponding author(s).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' E-mail(s): lizj@buaa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='cn;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Contributing authors: wangzixiang@buaa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='cn;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  jiaya@buaa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='cn;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' tonyliangli@buaa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='cn;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  liujiaheng@buaa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='cn;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' moying@buaa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='cn;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' bjq@buaa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='cn;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  hlt@cert.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='cn;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Abstract  Entity and relation extraction is a key task in information extraction,  where the output can be used for downstream NLP tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Existing  approaches for entity and relation extraction tasks mainly focus on  the English corpora and ignore other languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Thus, it is critical to  improving performance in a multilingual setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Meanwhile, multilingual  training is usually used to boost cross-lingual performance by trans-  ferring knowledge from languages (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', high-resource) to other (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  low-resource) languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' However, language interference usually exists  in multilingual tasks as the model parameters are shared among all  languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In this paper, we propose a two-stage multilingual train-  ing method and a joint model called Multilingual Entity and Relation  Extraction framework (mERE) to mitigate language interference across  languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Specifically, we randomly concatenate sentences in differ-  ent languages to train a Language-universal Aggregator (LA), which  narrows the distance of embedding representations by obtaining the  unified language representation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Then, we separate parameters to mit-  igate interference via tuning a Language-specific Switcher (LS), which  includes several independent sub-modules to refine the language-specific  1  arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='04434v1  [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='CL]  11 Jan 2023  Springer Nature 2021 LATEX template  2  Article Title  feature representation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' After that, to enhance the relational triple extrac-  tion, the sentence representations concatenated with the relation feature  are used to recognize the entities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Extensive experimental results show  that our method outperforms both the monolingual and multilingual  baseline methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Besides, we also perform detailed analysis to show  that mERE is lightweight but effective on relational triple extraction  and mERE is easy to transfer to other backbone models of multi-field  tasks, which further demonstrates the effectiveness of our method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Keywords: Joint extraction, Information extraction, Multilingual entity and  relation extraction, Relational triple  1 Introduction  Entity and relation extraction (ERE) contains two sub-tasks called named  entity recognition (NER) [1–4] and relation classification (RC) [5, 6], which is  the fundamental step of automatic knowledge graphs (KGs) [7] construction,  knowledge discovery and intelligent question answering system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The results of  ERE are typically described as a relational triple (h, r, t), where h and t are  the head entity and the tail entity, respectively, and r denotes the relation  between them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' For example, for the sentence “Big Ben is in UK.” with a  predefined relation called “Locate in”, an ideal relational triple of this sentence  is expressed as (Big Ben, Locate in, UK).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  As a large amount of data is available from different languages on the  Internet, it is important to utilize such valuable resources and develop multilin-  gual entity and relation extraction models, which can operate across language  barriers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' However, most existing methods propose to solve ERE on English  corpora, which can only deal with the monolingual extraction task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The main  reason is that many languages suffer from the scarcity of corpora in ERE.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Thus, multilingual training is proposed to help each other in a shared model,  where the well-trained knowledge of high-resource languages can be trans-  ferred to low-resource languages with a small amount of data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Recently, [8]  propose a multilingual dataset called SMiLER, which is the first work to apply  both monolingual and multilingual training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The authors in [8] introduce the  multilingual entity and relation extraction model (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', HERBERTa) without  considering interference across languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' However, such language interfer-  ence is prevalent in multilingual tasks because of parameter sharing [9–11].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  As shown in Figure 1, to mitigate interference among languages, we propose  to extract the feature representation of the corresponding language sentence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  First, to facilitate the cross-lingual transfer among different languages, mul-  tilingual representations are supposed to be closed under similar semantics  using cross-lingual sentence-level concatenation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Then, based on the shared  multilingual parameters, the language-specific representations derived from  the independent modules can mitigate interference among multiple languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  Article Title  3  Specifically, we propose a two-stage multilingual training method and an  我爱吃苹果。' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  amo le mele.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  I love apples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content="  J'adore les pommes." metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  …  Unified  Feature  Chinese  Feature  Italian  Feature  English  Feature  French  Feature  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1 This example includes 4 sentences from different languages, which express the same  meaning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The four arrows represent four independent sentence representations extracted  from different languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  effective model called multilingual Entity and Relation Extraction framework  (mERE) to address the multilingual ERE task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In the first stage, we utilize a  cross-lingual encoder to encode different language sentences and extract rela-  tions directly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Then, we train the joint model with our Language-universal  Aggregator (LA) to generate the unified language feature, which narrows the  distance of similar semantic representation across languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' LA consists of  a self-attention layer and is trained by random multi-sentences concatena-  tion, which is used to learn semantic similarities in multilingual training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In  the second stage, to alleviate the interference among languages, we freeze the  parameters of LA and cross-lingual encoder in the first stage and optimize the  independent parameters via fine-tuning the model with a Language-specific  Switcher (LS), which consists of several independent sub-modules to produce  the specific language features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Meanwhile, a selection mechanism is applied  to choose the optimal group of sub-modules from LS, which enables the sub-  module to share the same parameters with a certain group of languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Such  an automatic sub-module selection mechanism saves many model parameters  when the number of languages is large.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' After that, each token representation  is concatenated with the relation representation to enhance the recognition  of the positions of entities in a sentence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Finally, in mERE, we adopt joint  training to mitigate the error propagation problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  We conduct extensive experiments on the SMiLER benchmark of 14 lan-  guages with 36 relations (including no relation) in total.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The experimental  results demonstrate that our method outperforms previous monolingual and  multilingual ERE baseline methods by a large margin across languages, which  demonstrates that our method can effectively mitigate language interference  by improving representation quality among languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Besides, we conduct  detailed experiments to analyze how our method affects relational triple extrac-  tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Moreover, our method is simple but effective, and it is also easy to transfer  to different backbone models of multi-field tasks with lightweight modules.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  4  Article Title  2 Related Work  Information Extraction Information extraction mainly focuses on extract-  ing knowledge from unstructured text.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' A well-known system called Never-  Ending Language Learner was reading the Web for almost 10 years to collect  new instances of pre-defined relations and entity types [12].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Instead of the pre-  defined entity and relation types, Open Information Extraction (OpenIE) has  also attracted much attention during the past decade.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' A notable example is  TextRunner [13], which utilizes a syntactic parser to extract triples from the  Internet automatically.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Many systems have been proposed subsequently, such  as rule-based systems [14–16] and clause based systems [17, 18].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Recent super-  vised methods are divided into three categories based on different architectures:  (1) Generation-based models are typically sequence-to-sequence structure [19–  21].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2) Sequence labeling-based models using Begin Inside Outside (BIO)  or Subject Relation Object None (SRON) to label every word in a sentence  [22, 23].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (3) Span-based model takes advantage of span level feature which can  be sufficiently exploited [24].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Entity and Relation Extraction Early entity and relation extraction tasks  use a pipeline approach, which are two separate subtasks including named  entity recognition and relation classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' [25] first works on Recurrent Neu-  ral Network (RNN) based model for extraction, capturing the semantics of the  entity and its adjacent phrases through parsing trees.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' While [26] uses a syntac-  tic tree-based RNN model to add weights to the important phrases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' [27] first  used a Convolutional Neural Network (CNN) structure to fuse the extracted  word and sentence level features for extraction work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' [28] uses a CNN structure  based on a dependency tree to improve the performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' However, the pipeline  approach has inevitable deficiencies: (1) The architecture ignores the interac-  tions between entities and relations, causing the error propagation problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2)  Some of the extracted entities are redundant in the named entity recognition  phase, resulting in a degradation of performance in the relation classification  phase.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Most studies focus on the joint approach, which models entity recognition  and relation classification in the same network and naturally relieves error  propagation problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The initial joint models are feature-based methods that  heavily rely on NLP tools and manual efforts [29–32].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Recent joint models are  typically neural network-based methods, which benefit from their excellent fea-  ture learning capability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' SPTree [33] is the first joint model based on the neural  network method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Due to the two subtasks decoding with independent decoders  but sharing parameters of the same encoding layers, this architecture also is  known as parameters sharing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Following such kind of structure, [34] proposed  an LSTM-based network that decodes entities and a CNN network to classify  relations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' [35, 36] employ CRF to improve performance of entity recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  [37–40] use a pre-trained model called bidirectional encoder representation  from transformers (BERT) to improve the accuracy of entity recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' [41]  proposes a multi-feature fusion sentence representation and decoder sequence  annotation to handle the overlapping triples which are overlapped with one  Springer Nature 2021 LATEX template  Article Title  5  or two entities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Another architecture is joint decoding, which extracts entity  pairs and corresponding relations simultaneously in one stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' NovelTagging  [42] first proposes a tagging scheme to implement a joint decoding manner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  But it cannot figure out the overlapping problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The sequence-to-sequence  scheme [43–46] models relational triples as a sequence, which can naturally  deal with the nested entity and overlapping problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Multilingual Models Multilingual models are a type of model that per-  forms cross-lingual transfer among different languages, such as multilingual  pre-training [47–51] and machine translation [11, 52–54].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Specifically, mBERT  pre-trained on 104 languages in Wikipedia has a strong ability for cross-  lingual transfer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Multilingual neural machine translation (MNMT) trains a  single NMT model in multiple language pairs supporting translation direc-  tions between multiple languages by sharing parameters [55–58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Early studies  mainly utilize high-resource languages to help low-resource languages and  even perform zero-shot transfer translation [59, 60].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Recent studies focus on  designing language-specific components to mitigate the language interference  in shared parameters, especially on high-resource pairs [11, 61, 62].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Our method  boosts the sentence representation quality from superior unified representation  to further language-specific representation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Multilingual Entity and Relation Extraction Existing entity and rela-  tion extraction datasets are insufficient in diversity and size.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' English is always  used to be training corpora.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' [8] presents a new, large and diversified dataset  Samsung MultiLingual Entity and Relation Extraction (SMiLER) dataset to  entity and relation extraction both for English and multilingual setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' This  is currently the most comprehensive and largest multilingual dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  In this paper, we propose a multilingual entity and relation extraction  framework called mERE with two-stage training strategies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In the first stage,  we concatenate random sentences and use the self-attention mechanism [63]  to learn the unified representation across languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Inspired by MoE [64],  we use several sub-modules with a selection mechanism to learn the specific  representation of each language in the second stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Such two-stage learning  greatly improves the performance of relational triple extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  3 Methodology  In this section, we introduce the details of our training method for the multi-  lingual joint extraction model as shown in Figure 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We propose a two-stage  training strategy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In the first stage, we train a Language-universal Aggrega-  tor (LA) for learning the unified representations among multiple languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In  the second stage, we freeze the parameters and fine-tune the Language-specific  Switcher (LS), which is applied to select specific feature representations of  various languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  6  Article Title  FR: Tour Eiffel à Paris.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  EN: Big Ben is in UK.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  ES: España en Europa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  IT: Torre pendente di Pisa in Italia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  ….' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Cross-lingual Pretrained Encoder  Embeddings  Classifier  Relation  [CLS]  [CLS]  𝜃1  𝜃2  𝜃3  𝜃4  Language-universal Aggregator  Language-specific  Switcher  Entity1  Entity2  Big  Ben  UK  Weighted sum  NER  Concatenate  Encoder  RC  Switcher-based  Tuning  NER  LA  Encoder  RC  NER  LA  LS  Freeze  Multilingual  Training  Selection  Distribution  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 2 The left part shows the two-stage training strategy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The right part is our frame-  work with Language-universal Aggregator (LA) for unified representation generation and  Language-specific Switcher (LS) for language-specific feature extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We first train the  LA with a concatenation of 2 random sentence representations, which are denoted as the  green boxes (English) and yellow boxes (Italian) below the figure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Note that each sentence  representation is directly regarded as input of LA during the evaluation stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Then, we  freeze part of the parameters and fine-tune the LS with all sub-modules during the training  stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The figure illustrates 4 sub-modules of LS with a top-2 strategy during evaluation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1 Task Formulation  The goal of multilingual joint entity and relation extraction aims to identify all  possible relational triples from sentences in different languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Formally, given  a sentence X from multilingual corpora D = {Dn}N  n=1, where N represents  the number of the all languages Lall = {Ln}N  n=1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The probability of the target  triple Y = {s, r, o} is defined as below:  P(Y | X) = p(r | X;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' φ)p(s, o | X, r;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ϕ),  (1)  where r denotes relation, s and o are subject (head entity) and object (tail  entity), respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' p(r | X;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' φ) means relation is only related to sentence X,  and p(s, o | X, r;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ϕ) means the entity pair (s, o) is related to both sentence X  and the relation r that they shared.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2 Language-aggregation Training  We train the model with Language-universal Aggregator (LA) to learn the  unified representation, which effectively narrows the distance of semantic  representations across different languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' To obtain context representations  of each token from the multilingual sentences, we utilize the cross-lingual  pre-trained encoder for building a multilingual model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Given the sentence  XLn = {xLn  1 , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , xLn  i  , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , xLn  m } with m tokens (including [CLS], [SEP] and  Springer Nature 2021 LATEX template  Article Title  7  [PAD]), xLn  i  ∈ Rd is the i-th token embedding and d is the embedding size.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  The whole sentence is encoded by the cross-lingual pre-trained encoder:  hLn = H(XLn;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' φ),  (2)  where hLn = {hLn  1 , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , hLn  i  , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , hLn  m } ∈ Rm×d represents the encoded rep-  resentation and d is the hidden size.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' H denotes the cross-lingual pre-trained  encoder.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Meanwhile, a relation classifier W r ∈ Rd×U is used to project pooled  output vector hp (from the [CLS] token) to the relation rc, where U is the  number of relation types.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The relation extraction is defined as:  rc = hpW r,  (3)  To better learn the unified semantic representation among multiple lan-  guages, we randomly sample s sentences of different languages from the  training corpora to generate the cross-lingual representations using Equation 2  and concatenate them to obtain hcat = [h  LX1  1  , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , h  LXi  i  , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , hLXs  s  ], where LXi  denotes the language symbol of the i-th sentence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Considering that each token  needs to capture the dependency of inner-sentence and acquire semantic sim-  ilarity representation of inter-sentence among languages, we train LA which  applies the self-attention mechanism for fusing the information of the given  concatenated representation:  ˆhcat = SF(QKT  √ϵ )V  (4)  where Q = hcatWq, K = hcatWk and V = hcatWv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' SF represents the softmax  operation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The three-parameter matrices Wq, Wk, and Wv are trainable.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The  term 1/√ϵ is the scaling factor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ˆhcat = {ˆh  LX1  1  , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , ˆh  LXi  i  , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , ˆhLXs  s  } and ˆh  LXi  i  is  i-th element.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Instead of using language-specific features generated via Equation  8, we directly utilize each element representation in ˆh  LXi  i  to train the model  via Equation 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3 Language-specific Training  To acquire features of a specific language, we freeze the parameters of language  aggregation and cross-lingual encoder in the first training stage and fine-tune  the model with LS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' After obtaining the unified representation via LA, we  extract the language-specific features via the LS with the selection mechanism  from the unified representations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Given the language symbol Ln ∈ Lall(1 ≤ n ≤ N) and our LS θ =  {θt}T  t=1(1 ≤ t ≤ T , 1 ≤ T ≤ N), our selection mechanism is used to select  corresponding sub-modules θf(Ln), in which f(·) is a function that maps a lan-  guage to corresponding LS modules.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' To design an appropriate map function for  our selection mechanism, each sentence is prefixed to the corresponding lan-  guage symbol, which enables the model to correctly route sentences.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Besides,  Springer Nature 2021 LATEX template  8  Article Title  all sub-modules from LS attend to the selection procedure during the training  stage, which solves the undifferentiability problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Specifically, the function  ft(·) indicates the probability of selection of sub-module θt:  ft (Ln) =  exp  �  eLn  t  �  �T  i=1 exp  �  eLn  i  �  (5)  where eLn  i  is i-th element of the probability vector eLn = El[n]Wf.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' El ∈  RN×d denotes the look-up table for all language prefix embeddings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The router  matrix Wf ∈ Rd×T is used to project eLn which are normalized via a softmax  distribution over the total T modules.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  For each sub-module θt from θ, we utilize Eθt(·) to transform unified feature  representation ˆhLn into language-specific feature branch ˜hLn  θt :  ˜hLn  θt = Eθt(ˆhLn)  (6)  Eθt(ˆhLn) = LN  �  σ(ˆhLnWu)Wd + ˆhLn�  (7)  where ˆhLn ∈ Rm×d is an element of ˆhcat.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Wu ∈ Rd×b and Wd ∈ Rb×d are  projection matrices (b > d).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' σ is the ReLU activation function and LN(·) is  the layer normalization function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The right part of Figure 2 corresponds to  Equation 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  To ensure gradients are propagated to all sub-modules of LS {θt}T  t=1, we  apply the weighted average for obtaining the language-specific feature:  ˜hLn =  T  �  t=1  ft(Ln)Eθt  �  ˆhLn�  (8)  Note that for the whole process, function ft(Ln) in Equation 8 permits  differentiability of the router.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  In the evaluation stage, it is necessary to prune several sub-module branches  with the lowest selection probabilities to obtain the best performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' There-  fore, we use the top-K strategy to select the best k(1 ≤ k ≤ T ) sub-modules  with the highest probabilities to generate the language-specific representation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  When k = T indicates all sub-modules involved in the calculation which means  the selection mechanism is the same as the training stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The mapping pro-  cess is described as: Ln −→ {πLn  1 , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , πLn  i  , .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , πLn  k } ∈ Π(k), where πLn  i  is  one of the sub-module index that corresponds to language Ln and Π(k) is the  space of all k-length combinations of Ck  T in total.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  After obtaining the language-specific representation from LS, we create  four matrices to recognize the head and tail positions of two named entities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' To  enhance the accuracy of recognition, we add a relation feature that constrains  the extracted entities that are only related to the relevant relation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Formally,  given a language-specific representation ˜hLn ∈ Rm×d of the m-length sentence  Springer Nature 2021 LATEX template  Article Title  9  and the relation vector re retrieved from relation embedding table Er ∈ RI×d,  where I is the number of relations, the two entities are recognized as followed:  entityx = (η((˜hLn ⊕ re)Wy))Uy  (9)  where the symbol collection entity={head, tail}, x={start,end} and y =  {hs, he, ts, te}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We concatenate the relation vector with each token rep-  resentation to enhance the recognition of entities, namely ˜hLn ⊕ re  =  {[˜hLn  1 , re], .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , [˜hLn  i  , re], .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' , [˜hLn  m , re]} ∈ Rm×2d.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Wy ∈ R2d×d are four down  projection matrices and Uy ∈ Rd×1 are four index projection matrices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' η  denotes tanh activation function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Note that we use ground-truth relation as  input in training entity recognition, which conforms to the joint training  method in our architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4 Training Objective  Our model presented in Figure 2 is trained jointly on multilingual ERE cor-  pora.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We first train the model only using a multilingual training strategy for  our Language-universal Aggregator.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Based on the unified language representa-  tion, we fine-tune the model with Language-specific Switcher for learning the  language-specific feature in the next step.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The objective is to minimize the two  training loss functions which are defined below:  LLAT =  M  �  m=1  E(x,y)∼Dm[Lere(x, y;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Θ)]  (10)  LLST =  M  �  m=1  E(x,y)∼Dm[Lere(x, y;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Θ, θ)]  (11)  where D means multilingual entity and relation extraction training corpora  and M denotes the number of the samples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Θ indicates shared parameters and  θ is parameters in LS with selection mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Lere is the loss function for  entity and relation extraction, which is defined as below:  Lere = α  2 (Lstart  h  + L end  h  + L start  t  + L end  t  ) + βLrel  (12)  where each L with any superscript is a cross-entropy loss.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The subscripts with  h and t indicate the head entity and tail entity respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The start and end  of superscripts denote the first token index and last token index of an entity  separately.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Lrel is the loss function for relation classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' α and β are two  weights on entity recognition loss and relation classification loss respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  10  Article Title  4 Experiments  4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1 Datasets  We evaluate our model on the dataset SMiLER [8], which is the largest  and most diversified multilingual dataset for multilingual entity and relation  extraction tasks with 14 languages from 36 relation types.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The SMiLER con-  sists of about 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1M annotated sentences from Wikipedia and DBpedia, which  includes English (En), Korean (Ko), Italian (It), French (Fr), German (De),  Portuguese (Pt), Nederlands (Nl), Polish (Pl), Spanish (Es), Arabic (Ar), Rus-  sian (Ru), Swedish (Sv), Farsi (Fa), Ukrainian (Uk).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The relation types belong  to roughly nine domains: location, organization, person, animal, art, device,  measurement, event, and no relation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The statistics of SMiLER are shown in  Table 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' As the development set in SMiLER is not publicly available, we only  randomly extract the sentences from the training set to create new files with  the same split ratio as the original paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Table 1 The statistics of SMiLER dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' English corpora include full-size, middle-size,  and small-size.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The languages are ordered from high-resource languages (left) to  low-resource languages (right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Languages  EN-full  EN-mid  It  Fr  De  Pt  Nl  En-small  Ko  Pl  Es  Ar  Ru  Sv  Fa  Uk  sentences num.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  748k  269k  76k  62k  53k  45k  40k  35k  20k  17k  12k  9k  7k  5k  3k  1k  relation types  36  36  22  22  22  22  22  32  28  22  22  9  8  22  8  7  4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2 Implementation Details  We conduct experiments on SMiLER, 14 languages in total.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' EN-small is  treated as our English corpora.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We utilize mBERT as our cross-lingual encoder.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  We train our model with AdamW, the learning rate is 3e-5 and weight decay  is 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The batch size is set to 16 on Tesla V100 GPU.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The hidden size d is 768  and dimension b of projection matrices Wu and Wd is 1024.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The max sequence  length is 256 and we concatenate 2 sentences during the first training stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' For  the second training stage, we freeze most parameters in the first stage except  the relation classifier and 8 matrices used to predict entities from Equation 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  The sub-module number T of LS is set to 6 (2 layers for 3 sub-modules and 1  layer for the other).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The epoch is set to 5 at the first stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The max epoch of  the second stage is set to 8 with an early stopping mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The loss weights  are set to 2 in named entity recognition and 1 in relation classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  In the evaluation stage, we set k = 3 in the top-K strategy to select the  sub-modules in LS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We adopt standard micro-F1 metric to calculate scores on  the models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The extracted entity pair is regarded as correct if the predictions  of the head entity and tail entity are both the same as the ground truth.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' A  triple is treated as correct if the entity pair and the corresponding relation  type are all correct.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' no relation type is included in relation prediction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We  also add a mask for the relation that is not absent in a language.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  Article Title  11  4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3 Baselines  As far as we know, the SMiLER is a new dataset and thus only an existing  method for multilingual ERE without publishing source code.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The relevant  task is cross-lingual relation classification, which is also few in studies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' There-  fore, we reproduce the following competitive baselines to compare with our  proposed approach for a fair comparison:  HEBERTa [8]: A multilingual entity and relation extraction framework  called Hybrid Entity and Relation extraction BERT, which achieves the  state-of-the-art performance on SMiLER.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' HERBERTa uses a pipeline train-  ing manner that combines two independent BERT models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The first  sub-model classifies the input sequence as one of 36 pre-defined relations  (including no relation).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The relation generated from the first sub-model is  then fed to the second BERT and concatenated with the same input sequence  as the input of the second model for entity recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  mBERT [65]: A cross-lingual model first uses the mBERT as a backbone  for RC, which is trained on 104 languages with the corresponding Wikipedia  dumps.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We reproduce the results with the code shared at https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  com/boun-tabi/RELX  MTMB [65]: A multilingual pre-training scheme called Matching the Mul-  tilingual Blanks (MTMB).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The framework shows several advantages against  the mBERT on monolingual tasks and achieves significant improvements in  cross-lingual transfer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Note that this framework is only designed for RC and  not adapted to entity and relation extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Therefore, we simply modified  the output layer of the baseline to conduct the ERE task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  In addition to the above baselines, we also build a simplified multilin-  gual joint entity and relation extraction framework called mERE-LS-LA as a  basic structure which is concatenated relation representation with the sentence  representation to enhance the extraction performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Table 2 The F1 scores of different models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' * denotes the model is reproduced by us on  our experiment settings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' - denotes that the language data is not involved both in the  training and the evaluation stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' MONO, EURO, and SVO mean training data in 3  different language groups.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The languages are ordered from high-resource languages (left) to  low-resource languages (right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The bold font number is the best score in each language.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Test Sets  AVG  It  Fr  De  Pt  Nl  En  Ko  Pl  Es  Ar  Ru  Sv  Fa  Uk  HERBERTa*  75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  60.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  60.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  88.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  mBERT*1  75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  59.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  61.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  89.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  MTMB*1  75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  67.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  58.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  58.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  mERE  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  mERE (EURO)  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2 62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2 65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2 mERE (SVO)  75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1 81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7 83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7 80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  mERE-LS  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  69.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  mERE-LS-LA (MONO)  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  67.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  58.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  48.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  66.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  55.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  mERE-LS-LA  76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  69.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  60.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  60.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  1We modified the output layer to implement the entity recognition to accommodate the  ERE task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We train the model in the joint training method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  12  Article Title  4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4 Models and Languages Comparison  The results presented from the Tables are rounded to one decimal place.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' From  Table 2, our method improves multilingual baselines by a large margin over pre-  vious baselines.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' There is a 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3% improvement on averaged F1 score compared  with the previous strongest baseline MTMB which outperforms HERBERTa  due to its strong multilingual pre-training scheme.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Our mERE achieves the  best scores on 8 out of 14 languages, especially on high-resource languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  The other 5 out of 6 languages achieve the second-best scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Surprisingly,  even our baseline mERE-LS-LA has 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9% improvement over the MTMB.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' It  seems that our basic structure is more effective on multilingual entity and rela-  tion extraction tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Compared with mERE-LS that only uses LA, our full  model mERE has nearly 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7% F1 value improvement on average and yields  similar or higher results on 13 languages except for Sv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The improvement can  be attributed to our switcher-based language-specific training strategy, which  finally extracts accurate information for entity recognition in each language.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Compared with our baseline mERE-LS-LA, our full model mERE has nearly  1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4% F1 value improvement on average which means mERE-LS also has nearly  0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7% F1 value improvement on average.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' All such impressive results demon-  strate that our full model mERE truly enhances the representation quality  and mitigates language interference to a certain extent.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  We set several language groups to analyze the impact of different languages:  (1)MONO: 14 languages in monolingual training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2)EURO: It, Fr, Pt, De,  Es, En.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (3)SVO2: EURO, Ru, Sv, Nl, Pl, Uk.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The default is all languages  in multilingual training from Table 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Compared with mERE-LS-LA training  in multilingual corpora, we can observe that multilingual training achieves  much higher results than mERE-LS-LA (MONO) monolingual training from  Table 2, especially on low-resource languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Such as improvements of Uk  (25%), Fa (11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1%), and Ru (11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' It demonstrates that languages with less  training data can benefit most from high-resource languages in multilingual  training including ERE tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The results of the EURO family group are close  to mERE.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' It is worth noting that Es achieves the best score in the EURO  group.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We conclude that Es benefits a lot from similarities of languages that  are in the same language family even with less training data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In the SVO  group, we can also visualize that most languages in EURO decrease slightly  with the interference of other non-EURO languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The different language  families, or the languages with a big difference in syntactic structures might  be the main interference among languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' However, compared with mERE  (SVO), mERE yields the same results on low-resource languages and somewhat  higher results on high-resource languages even the three non-SVO (Fa, Ar, and  Ko) data involved during the training stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We suppose that these non-SVO  languages which are big different from others and are all low-resource may  facilitate distinguishing high-resource languages in learning language-specific  2SVO stands for the relative position of the Subject, Verb, and Object in the typical affirmative  sentence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We treat Korean, Farsi, and Arabic as non-SVO languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Arabic is VSO, while Korean  and Farsi are SOV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  Article Title  13  features due to each sub-module from LS being independent, without sharing  parameters in the same space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Lastly, we also observe some duplicated F1  scores across low-resource languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' This phenomenon is caused by a small  number of sentences in test sets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5 Entity and Relation Analysis  Figure 3 shows F1 scores of relation and entity pair of mERE and mERE-LS-  LA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We can observe that the relation classification seems to be easier than the  named entity recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The correctness of entity pair extraction is the main  bottleneck of the model performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' With the help of our LA and LS, mERE  achieves higher results on entity pair recognition compared with mERE-LS-  LA in general.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Surprisingly, we can visualize that the performance of relation  classification also has a slight improvement in mERE.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We conclude that the  improvement of the named entity recognition facilitates relation classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Since information interaction between two sub-tasks can benefit each other in  the joint training architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  50  55  60  65  70  75  80  85  90  95  100  Total  It  Fr  De  Pt  Nl  En  Ko  Pl  Es  Ar  Ru  Sv  Fa  Uk  Relation(mERE)  Entity Pair(mERE)  Relation(mERE-LS-LA)  Entity Pair(mERE-LS-LA)  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 3 The F1 scores of relations and entity pairs on all languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  F1 scores of detailed relation labels are shown in Figure 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Most of the  relations achieve higher F1 scores across languages, such as “no relation” and  “has-type”.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Part of relations differs widely across languages, such as relation  “has-child”(F1 = 100 on Nl, F1 = 33 on De, F1 = 0 on Es).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The big difference  is caused by the number of relations of training data in each language.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' For  some relations that occur F1 = 0 scores, we find out the relations (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='g won-  award on Nl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' has-parent on Pl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' has-child on Es) are only one test sample.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Such low results for some languages could be explained by a smaller number  of relations in the test set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Springer Nature 2021 LATEX template  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='14  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Article Title  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='En  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='It  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Fr  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Pt  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Es  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='De  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Ar  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Nl  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Ru  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Pl  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Uk  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Sv  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Fa  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Ko  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='no_relation  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='is-where  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='birth-place  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-type  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='movie-has-director  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-occupation  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='from-country  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-genre  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-author  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-population  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='headquarters  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='is-member-of  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org-has-member  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-parent  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org-has-founder  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-spouse  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='won-award  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-nationality  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org-leader  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='starring  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-edu  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-child  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='event-year  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-sibling  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-length  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='invented-when  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-tourist-attraction  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-lifespan  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='first-product  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-height  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-highest-mountain  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='invented-by  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='has-weight  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='post-code  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='loc-leader  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='eats  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='85  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='86  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='91  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='86  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='75  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='97  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='90  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='96  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='88  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='81  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='90  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='85  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='93  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='97  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='60  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='89  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='91  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='89  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='89  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='94  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='84  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='89  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='50  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='81  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='84  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='97  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='95  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='97  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='91  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='95  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='94  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='76  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='99  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='99  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='59  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='72  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='68  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='79  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='83  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='70  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='96  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='98  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='25  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='75  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='30  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='57  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='56  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='66  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='72  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='86  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='91  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='83  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='75  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='86  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='93  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='94  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='60  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='94  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='96  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='99  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='83  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='80  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='78  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='77  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='77  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='88  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='82  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='91  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='96  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='88  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='95  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='88  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='86  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='88  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='57  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='83  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='90  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='89  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='89  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='90  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='60  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='83  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='86  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='50  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='89  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='95  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='50  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='50  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='79  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='63  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='75  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='91  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='75  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='80  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='86  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='50  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='33  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='92  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='33  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='67  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='87  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='83  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='62  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='80  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='78  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='50  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='88  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='50  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='56  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='20  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='40  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='60  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='80  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='100  ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 4 The F1 scores of all relation labels on all languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The darker color means a higher  F1 score, while the lighter color means a lower F1 score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Figure 5 shows F1 scores of head entities and tail entities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We can observe  that F1 scores of head entities are much higher than tail entities among most  languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' It seems that head entities are easier to be recognized than tail  entities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' It is because the head entity always occurs at the beginning position  of the sentence and thus the model probably memorizes the position, while the  tail entity does not have any consistent position which is hard to predict.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6 Ablation Study  Sentences Concatenation To validate the effect of the number of sentences  for learning the unified features among different languages, we conduct sev-  eral experiments on the different numbers of sentences in concatenation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We  learn from Figure 6 that there are evident F1 improvements with LA on dif-  ferent concatenation numbers of sentences over only one sentence encoding.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  The multilingual model obtains the best performance when concatenating with  the sentence pair.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The increasing number of concatenated sentences has a  slight decrease in performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We conjecture that increasing the number of  sentences may also bring somewhat interference.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  Article Title  15  50  55  60  65  70  75  80  85  90  95  100  Total It  Fr  De  Pt  Nl  En  Ko  Pl  Es  Ar  Ru  Sv  Fa  Uk  head entity  tail entity  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 5 The performance of head entities (blue bar) and tail entities (orange bar) on different  languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  1  2  3  4  Languages  76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  F1 score  mERE-LS  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 6 The performance of sentences concatenation in the first training stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Selection Mechanism To observe how the selection mechanism affects our  model performance, we also train one-to-one sub-modules of LS called mERE14  without using the selection mechanism in the second training stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Each inde-  pendent sub-module corresponds to a language and each sentence is routed via  a language prefix which represents the number of sub-module.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We can visualize  from Figure 7 that increasing the number of parameters also improves obvi-  ously over mERE-LS-LA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Nonetheless, the mERE14 will suffer from the sharp  increasing training time and inference time, and big space consumption when  the number of languages is large enough.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Instead of increasing parameters,  our Language-specific Switcher can effectively ameliorate extraction quality  with only slight extra parameters and less time consumption.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Since similar  languages tend to select the same sub-modules from our LS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The mERE saves  Springer Nature 2021 LATEX template  16  Article Title  nearly 700M model capacity in our statistics and achieves better performance  among most languages compared with mERE14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' It is obvious that mERE is  light and easy to transfer to other multi-field tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Selection Distribution  It  Fr  De  Pt  Nl  En  Ko  Pl  Es  Ar  Ru  Sv  Fa  Uk  Languages  60  62  64  66  68  70  72  74  76  78  80  82  84  86  88  90  92  94  F1 scores  mERE-LS-LA  mERE14  mERE  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 7 The performance of three models on 14 languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' mERE14 utilizes 14 one-to-one  sub-modules of LS without the selection mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Each sub-module corresponds to a  language.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Figure 8 illustrates the heatmap of selection probability on 6 sub-modules from  LS for each language.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' For each sub-module from top to bottom in Figure 8,  we can visualize θ1 pays more attention to low-resource languages while θ4,  θ5, and θ6 pay more attention to languages from the EURO family, which  are mostly high-resource languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The θ2 and θ3 seem to be more balanced  on parameters sharing of languages except for 2 or 3 prominent languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  We conclude that some sub-modules are mainly used to extract features from  similar languages and others are used to assist the specific languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  For each language from left to right, we can visualize that the selected  sub-modules with higher probabilities are easy to distinguish in high-resource  languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In contrast, the selection probabilities across all sub-modules are  relatively similar on low-resource languages in total.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We conclude that train-  ing data is rich enough to determine which way to route on high-resource  languages and a more balanced selection decision is made on less training  data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' It is learned from Figure 8 that there are nearly 3 out of 6 prominently  higher selection probabilities on the high-resource languages, and so do the  low-resource languages with careful observation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' It proves that only 3 sub-  modules play the dominant role in refining the language-specific feature for  each language.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' To avoid interference from the other irrelevant sub-modules,  we adopt a top-K strategy to filter out 6 − k sub-modules with lower selec-  tion probabilities in the evaluation stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The top-6 strategy means selecting  all sub-modules, which is the same as the training stage and the performance  is relatively low (77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='74) on average, while our mERE achieves the best perfor-  mance (77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='87) when adopting the top-3 strategy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' It demonstrates that filtering  out the least important sub-modules is necessary to enhance the prediction  quality, which also reduces the redundant parameters in the evaluation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The  Springer Nature 2021 LATEX template  Article Title  17  top-1 achieves the worst performance (77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='60), which demonstrates the part of  sub-modules are also helpful for the task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Therefore, the best performance is  obtained when the k value is balanced in all languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Layer Number of  It  Fr  De  Pt  Nl  En  Ko  Pl  Es  Ar  Ru  Sv  Fa  Uk  1  2  3  4  5  6  Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 8 The selection probability distributions of 6 sub-modules from LS on 14 languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  The sub-modules {θ}6  1 are numbered from 1 to 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The languages are ordered from high-  resource languages (left) to low-resource languages (right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The darker color means a higher  selection probability to the corresponding sub-module and a lower probability to select a  certain sub-module when the color is lighter.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Language-specific Switcher Table 3 used to evaluate the effect of the layer  number of LS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' We divide the 6 sub-modules into 2 groups (each group has the  same layer number) with different combinations of layer numbers to accommo-  date the scenarios, such as high- and low-resource language feature extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  From Table 3, we can observe that the combination 1-2 achieves the best F1  score on average.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The combinations which are set to 1-1 and 4-4 also achieve  better performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' With the increase or decrease of the layer number to a cer-  tain degree, the performances are almost the same, which maintains relatively  low averaged F1 scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The full layer number combination 4-4 is an exception  in the case, which demonstrates the performance still can be improved when  the model capacity is large enough.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' According to the outcomes from Table 3,  we conclude that the layer number of LS obviously impacts the results, with  the best results attained when a balance is reached.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  5 Conclusion  In this paper, we introduce a two-stage training method and a robust frame-  work called mERE for multilingual entity and relation extraction, which  ameliorates the sentence representation quality and mitigates the language  interference among multiple languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Specifically, we first learn the gener-  alities across all languages to obtain the unified language representation via  the Language-universal Aggregator and then learn the specialties of each lan-  guage via the Language-specific Switcher.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Experimental results demonstrate  Springer Nature 2021 LATEX template  18  Article Title  Table 3 The different layer numbers of sub-modules.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Every 3 sub-modules in a group has  the same layer numbers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Layer Num.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='01 and Layer Num.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='02 denote the layer number of the  first group and second group respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Layer Num.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='01  Layer Num.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='02  AVG  IT  FR  DE  PT  NL  EN  KO  PL  ES  AR  RU  SV  FA  UK  1  1  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  69.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  63.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  63.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  1  2  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  1  3  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  69.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  1  4  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  2  2  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  66.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  2  3  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  2  4  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  3  3  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  3  4  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='4  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  4  4  77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='9  81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='5  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1  83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='7  79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='6  80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='0  that our method significantly outperforms both monolingual and multilingual  ERE baselines, which demonstrates that our framework can extract relational  triples among various languages well.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Moreover, our framework is also light  and easy to transfer to other backbone models of multi-field tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  In the future, we will pay more attention to complex multilingual relational  triple extraction, such as overlapping relational triples or multiple relational  triples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Besides, we will also do further research on a better contextual repre-  sentation among multiple languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Although there is a long way to experience  in multilingual entity and relation extraction tasks, it is important to inves-  tigate the valuable structured information in many other languages for the  downstream NLP tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Acknowledgments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  This work was supported in part by the National Nat-  ural Science Foundation of China (Grant Nos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 62276017, U1636211, 61672081),  the 2022 Tencent Big Travel Rhino-Bird Special Research Program, and the  Fund of the State Key Laboratory of Software Development Environment  (Grant No.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' SKLSDE-2021ZX-18).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  References  [1] Huang, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Xu, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yu, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Bidirectional LSTM-CRF models for sequence  tagging.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' CoRR abs/1508.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='01991 (2015) 1508.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='01991  [2] Wang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Shou, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chen, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chen, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Pyramid: A layered model for  nested named entity recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Jurafsky, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chai, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Schluter,  N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tetreault, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 58th Annual Meet-  ing of the Association for Computational Linguistics, ACL 2020,  Online, July 5-10, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 5918–5928.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Computational  Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='acl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='525.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='acl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='525  [3] Joshi, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chen, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Weld, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zettlemoyer, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Levy, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=':  Spanbert: Improving pre-training by representing and predicting spans.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Trans.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Assoc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Comput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Linguistics 8, 64–77 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  1162/tacl a 00300  Springer Nature 2021 LATEX template  Article Title  19  [4] Tan, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Shen, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Lu, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhuang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': A sequence-to-set net-  work for nested named entity recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Zhou, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (ed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings  of the Thirtieth International Joint Conference on Artificial Intelligence,  IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  3936–3942.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='24963/ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2021/542.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='24963/ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2021/542  [5] Levy, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Seo, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Choi, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zettlemoyer, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Zero-shot relation extraction  via reading comprehension.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Levy, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Specia, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings  of the 21st Conference on Computational Natural Language Learning  (CoNLL 2017), Vancouver, Canada, August 3-4, 2017, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 333–342.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Asso-  ciation for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  18653/v1/K17-1034.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/K17-1034  [6] Li, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wang, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yang, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chen, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Logic-guided  semantic representation learning for zero-shot relation classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In:  Scott, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Bel, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zong, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 28th International  Conference on Computational Linguistics, COLING 2020, Barcelona,  Spain (Online), December 8-13, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 2967–2978.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' International Com-  mittee on Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='coling-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='265.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='coling-  main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='265  [7] Lin, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Mao, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Xu, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Cambria, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Fusing topology contexts  and logical rules in language models for knowledge graph completion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Inf.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Fusion 90, 253–264 (2023).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1016/j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='inffus.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='09.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='020  [8] Seganti, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Firlag, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Skowronska, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Satlawa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Andruszkiewicz, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=':  Multilingual entity and relation extraction dataset and model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Merlo,  P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tiedemann, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tsarfaty, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 16th Conference  of the European Chapter of the Association for Computational Linguis-  tics: Main Volume, EACL 2021, Online, April 19 - 23, 2021, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1946–1955.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Association for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/  10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='eacl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='166.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='eacl-  main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='166  [9] Wang, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Lipton, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tsvetkov, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': On negative interference in mul-  tilingual models: Findings and A meta-learning treatment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Webber,  B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Cohn, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', He, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 2020 Confer-  ence on Empirical Methods in Natural Language Processing, EMNLP  2020, Online, November 16-20, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 4438–4450.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Com-  putational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  emnlp-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='359.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='emnlp-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='359  [10] Gong, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Genzel, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Adaptive sparse transformer for multilingual  translation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' CoRR abs/2104.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='07358 (2021) 2104.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='07358  Springer Nature 2021 LATEX template  20  Article Title  [11] Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yin, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ma, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wei, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': High-resource  language-specific training for multilingual neural machine translation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In:  Raedt, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (ed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the Thirty-First International Joint Con-  ference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July  2022, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 4461–4467.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='24963/ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  2022/619.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='24963/ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2022/619  [12] Mitchell, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Cohen, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Jr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Talukdar, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yang, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Betteridge, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Carlson, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Mishra, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Gardner, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Kisiel, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Krish-  namurthy, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Lao, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Mazaitis, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Mohamed, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Nakashole, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Platanios, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ritter, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Samadi, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Settles, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wang, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wijaya,  D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Gupta, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chen, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Saparov, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Greaves, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Welling, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Never-  ending learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Commun.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ACM 61(5), 103–115 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/  10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1145/3191513  [13] Yates, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Banko, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Broadhead, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Cafarella, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Etzioni, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Soder-  land, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Textrunner: Open information extraction on the web.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Sidner,  C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Schultz, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Stone, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhai, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Human Language Technology  Conference of the North American Chapter of the Association of Com-  putational Linguistics, Proceedings, April 22-27, 2007, Rochester, New  York, USA, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 25–26.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The Association for Computational Linguistics,  ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2007).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/N07-4013/  [14] Fader, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Soderland, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Etzioni, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Identifying relations for open infor-  mation extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Proceedings of the 2011 Conference on Empirical  Methods in Natural Language Processing, EMNLP 2011, 27-31 July 2011,  John McIntyre Conference Centre, Edinburgh, UK, A Meeting of SIG-  DAT, a Special Interest Group of The ACL, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1535–1545.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ACL, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  (2011).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/D11-1142/  [15] de S´a Mesquita, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Schmidek, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Barbosa, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Effectiveness and efficiency  of open relation extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Proceedings of the 2013 Conference on  Empirical Methods in Natural Language Processing, EMNLP 2013, 18-21  October 2013, Grand Hyatt Seattle, Seattle, Washington, USA, A Meeting  of SIGDAT, a Special Interest Group of The ACL, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 447–457.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ACL, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  (2013).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/D13-1043/  [16] White, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Reisinger, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Sakaguchi, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Vieira, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Rudinger, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Rawlins, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Durme, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Universal decompositional  semantics on universal dependencies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Su, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Carreras, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Duh,  K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 2016 Conference on Empirical Meth-  ods in Natural Language Processing, EMNLP 2016, Austin, Texas,  USA, November 1-4, 2016, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1713–1723.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The Association for Compu-  tational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/d16-1177.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/d16-1177  Springer Nature 2021 LATEX template  Article Title  21  [17] Corro, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Gemulla, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Clausie: clause-based open information extrac-  tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Schwabe, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Almeida, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Glaser, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Baeza-Yates,  R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Moon, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') 22nd International World Wide Web Con-  ference, WWW ’13, Rio de Janeiro, Brazil, May 13-17, 2013, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  355–366.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' International World Wide Web Conferences Steering Com-  mittee / ACM, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2013).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1145/2488388.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2488420.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1145/2488388.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2488420  [18] Angeli, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Premkumar, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Manning, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' : Leveraging linguistic  structure for open domain information extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Proceedings of the  53rd Annual Meeting of the Association for Computational Linguistics  and the 7th International Joint Conference on Natural Language Pro-  cessing of the Asian Federation of Natural Language Processing, ACL  2015, July 26-31, 2015, Beijing, China, Volume 1: Long Papers, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  344–354.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The Association for Computer Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2015).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https:  //doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3115/v1/p15-1034.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3115/v1/p15-1034  [19] Cui, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wei, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhou, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Neural open information extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In:  Gurevych, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Miyao, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 56th Annual Meeting  of the Association for Computational Linguistics, ACL 2018, Melbourne,  Australia, July 15-20, 2018, Volume 2: Short Papers, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 407–413.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Associa-  tion for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/  v1/P18-2065.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/P18-2065/  [20] Kolluru, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Aggarwal, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Rathore, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Mausam, Chakrabarti, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Imojie:  Iterative memory-based joint open information extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Juraf-  sky, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chai, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Schluter, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tetreault, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of  the 58th Annual Meeting of the Association for Computational Lin-  guistics, ACL 2020, Online, July 5-10, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 5871–5886.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association  for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/  2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='acl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='521.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='acl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='521  [21] Li, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wang, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Low resource quantitative information extraction  via structure searching and prefix-based text generation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' AAAI Press, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  (2023)  [22] Stanovsky, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Michael, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zettlemoyer, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Dagan, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Supervised open  information extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Walker, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ji, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Stent, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Pro-  ceedings of the 2018 Conference of the North American Chapter of  the Association for Computational Linguistics: Human Language Tech-  nologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6,  2018, Volume 1 (Long Papers), pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 885–895.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Compu-  tational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/n18-1081.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/n18-1081  [23] Kolluru, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Adlakha, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Aggarwal, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Mausam, Chakrabarti, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=':  Springer Nature 2021 LATEX template  22  Article Title  Openie6: Iterative grid labeling and coordination analysis for open  information  extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  In:  Webber,  B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Cohn,  T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  He,  Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Liu,  Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 2020 Conference on Empirical Meth-  ods in Natural Language Processing, EMNLP 2020, Online, Novem-  ber 16-20, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 3748–3761.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Computational Lin-  guistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='emnlp-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='306.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='emnlp-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='306  [24] Zhan, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhao, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Span model for open information extraction on  accurate corpus.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: The Thirty-Fourth AAAI Conference on Artificial  Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of  Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Sympo-  sium on Educational Advances in Artificial Intelligence, EAAI 2020, New  York, NY, USA, February 7-12, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 9523–9530.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' AAAI Press, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  (2020)  [25] Socher, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Huval, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Manning, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ng, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' : Semantic composition-  ality through recursive matrix-vector spaces.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Tsujii, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Henderson, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Pasca, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 2012 Joint Conference on Empiri-  cal Methods in Natural Language Processing and Computational Natural  Language Learning, EMNLP-CoNLL 2012, July 12-14, 2012, Jeju Island,  Korea, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1201–1211.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ACL, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2012).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/D12-  1110/  [26] Hashimoto, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Miwa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tsuruoka, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chikayama, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Simple cus-  tomization of recursive neural networks for semantic relation classifica-  tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Proceedings of the 2013 Conference on Empirical Methods in  Natural Language Processing, EMNLP 2013, 18-21 October 2013, Grand  Hyatt Seattle, Seattle, Washington, USA, A Meeting of SIGDAT, a  Special Interest Group of The ACL, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1372–1376.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ACL, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2013).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/D13-1137/  [27] Zeng, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Lai, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhou, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhao, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Relation classification via  convolutional deep neural network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Hajic, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tsujii, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') COLING  2014, 25th International Conference on Computational Linguistics, Pro-  ceedings of the Conference: Technical Papers, August 23-29, 2014, Dublin,  Ireland, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 2335–2344.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ACL, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/C14-  1220/  [28] Xu, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Feng, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Huang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhao, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Semantic relation classifi-  cation via convolutional neural networks with simple negative sam-  pling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: M`arquez, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Callison-Burch, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Su, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Pighin, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Marton,  Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 2015 Conference on Empirical Meth-  ods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal,  September 17-21, 2015, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 536–540.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The Association for Computa-  tional Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2015).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/d15-1062.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  Article Title  23  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/d15-1062  [29] Roth, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yih, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': A linear programming formulation for global inference  in natural language tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Ng, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Riloff, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceed-  ings of the Eighth Conference on Computational Natural Language  Learning, CoNLL 2004, Held in Cooperation with HLT-NAACL 2004,  Boston, Massachusetts, USA, May 6-7, 2004, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1–8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ACL, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2004).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/W04-2401/  [30] Kate, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Mooney, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Joint entity and relation extraction using card-  pyramid parsing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Lapata, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Sarkar, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the  Fourteenth Conference on Computational Natural Language Learning,  CoNLL 2010, Uppsala, Sweden, July 15-16, 2010, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 203–212.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ACL, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/W10-2924/  [31] Yu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Lam, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Jointly identifying entities and extracting relations  in encyclopedia text via A graphical model approach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Huang, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Jurafsky, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') COLING 2010, 23rd International Conference on  Computational Linguistics, Posters Volume, 23-27 August 2010, Beijing,  China, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1399–1407.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Chinese Information Processing Society of China,  ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/C10-2160/  [32] Li, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ji, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Incremental joint extraction of entity mentions and rela-  tions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Proceedings of the 52nd Annual Meeting of the Association  for Computational Linguistics, ACL 2014, June 22-27, 2014, Baltimore,  MD, USA, Volume 1: Long Papers, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 402–412.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The Association for  Computer Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3115/v1/p14-1038.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3115/v1/p14-1038  [33] Miwa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Bansal, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': End-to-end relation extraction using lstms on  sequences and tree structures.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Proceedings of the 54th Annual Meet-  ing of the Association for Computational Linguistics, ACL 2016, August  7-12, 2016, Berlin, Germany, Volume 1: Long Papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The Association for  Computer Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/p16-1105.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/p16-1105  [34] Zheng, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Hao, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Lu, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Bao, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Xu, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Hao, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Xu, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Joint  entity and relation extraction based on a hybrid neural network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Neuro-  computing 257, 59–66 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1016/j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='neucom.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2016.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  075  [35] Tan, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhao, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wang, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Xiao, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Jointly extracting multiple triplets  with multilayer translation constraints.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: The Thirty-Third AAAI Con-  ference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative  Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth  AAAI Symposium on Educational Advances in Artificial Intelligence,  EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Springer Nature 2021 LATEX template  24  Article Title  7080–7087.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' AAAI Press, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1609/aaai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='v33i01.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  33017080.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1609/aaai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='v33i01.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='33017080  [36] Liu, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chen, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wang, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Xu, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Attention  as relation: Learning supervised multi-head self-attention for relation  extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Bessiere, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (ed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the Twenty-Ninth Inter-  national Joint Conference on Artificial Intelligence, IJCAI 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  3787–3793.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='24963/ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2020/524.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='24963/ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2020/524  [37] Wadden, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wennberg, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Luan, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Hajishirzi, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Entity, relation,  and event extraction with contextualized span representations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Inui,  K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Jiang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ng, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wan, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 2019 Confer-  ence on Empirical Methods in Natural Language Processing and the 9th  International Joint Conference on Natural Language Processing, EMNLP-  IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 5783–5788.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Association for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/  10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/D19-1585.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/D19-1585  [38] Eberts, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ulges, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Span-based joint entity and relation extraction  with transformer pre-training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Giacomo, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Catal´a, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Dilkina,  B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Milano, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Barro, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Bugar´ın, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Lang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') ECAI 2020 - 24th  European Conference on Artificial Intelligence, 29 August-8 September  2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 -  Including 10th Conference on Prestigious Applications of Artificial Intel-  ligence (PAIS 2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Frontiers in Artificial Intelligence and Applications,  vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 325, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 2006–2013.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' IOS Press, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3233/  FAIA200321.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3233/FAIA200321  [39] Ji, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yu, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ma, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wu, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tan, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Span-based  joint entity and relation extraction with attention-based span-specific  and contextual semantic representations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Scott, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Bel, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zong,  C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 28th International Conference on Compu-  tational Linguistics, COLING 2020, Barcelona, Spain (Online), Decem-  ber 8-13, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 88–99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' International Committee on Computational  Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='coling-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='coling-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='8  [40] Qiao, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zou, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Huang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Fang, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chen, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': A joint model  for entity and relation extraction based on BERT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Neural Comput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Appl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  34(5), 3471–3481 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1007/s00521-021-05815-z  [41] Lai, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Cheng, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ye, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': RMAN: relational  multi-head attention neural network for joint extraction of entities and  relations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Appl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Intell.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 52(3), 3132–3142 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1007/  s10489-021-02600-2  Springer Nature 2021 LATEX template  Article Title  25  [42] Zheng, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wang, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Bao, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Hao, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhou, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Xu, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Joint extraction  of entities and relations based on a novel tagging scheme.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Barzilay, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Kan, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 55th Annual Meeting of the Association  for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 -  August 4, Volume 1: Long Papers, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1227–1236.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Compu-  tational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/P17-1113.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/P17-1113  [43] Zeng, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zeng, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', He, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhao, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Extracting relational facts  by an end-to-end neural model with copy mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Gurevych, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Miyao, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 56th Annual Meeting of the Associa-  tion for Computational Linguistics, ACL 2018, Melbourne, Australia, July  15-20, 2018, Volume 1: Long Papers, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 506–514.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Compu-  tational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/P18-1047.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/P18-1047/  [44] Zeng, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', He, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zeng, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhao, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Learning the extrac-  tion order of multiple relational facts in a sentence with reinforcement  learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Inui, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Jiang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ng, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wan, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the  2019 Conference on Empirical Methods in Natural Language Processing  and the 9th International Joint Conference on Natural Language Process-  ing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  367–377.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https:  //doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/D19-1035.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/D19-1035  [45] Nayak, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ng, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' : Effective modeling of encoder-decoder archi-  tecture  for  joint  entity  and  relation  extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  In:  The  Thirty-  Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The  Thirty-Second Innovative Applications of Artificial Intelligence Con-  ference, IAAI 2020, The Tenth AAAI Symposium on Educational  Advances  in  Artificial  Intelligence,  EAAI  2020,  New  York,  NY,  USA, February 7-12, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 8528–8535.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' AAAI Press, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://ojs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='aaai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/index.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='php/AAAI/article/view/6374  [46] Wang, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yang, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', He, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': A triple relation  network for joint entity and relation extraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Electronics 11(10) (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='3390/electronics11101535  [47] Devlin, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chang, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Lee, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Toutanova, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': BERT: pre-training of  deep bidirectional transformers for language understanding.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Burstein,  J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Doran, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Solorio, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 2019 Conference of  the North American Chapter of the Association for Computational Lin-  guistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis,  MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  4171–4186.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https:  //doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/n19-1423.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/n19-1423  Springer Nature 2021 LATEX template  26  Article Title  [48] Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ma, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wu, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhou, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Alternating language  modeling for cross-lingual pre-training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: AAAI 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 9386–9393  (2020)  [49] Conneau,  A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Khandelwal,  K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Goyal,  N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Chaudhary,  V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Wen-  zek, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Guzm´an, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Grave, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ott, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zettlemoyer, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Stoy-  anov, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Unsupervised cross-lingual representation learning at scale.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  In:  Proceedings  of  the  58th  Annual  Meeting  of  the  Association  for Computational Linguistics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Computational Lin-  guistics, Online (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='acl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='747.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='acl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='747  [50] Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ma, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Dong, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Huang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Huang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yin, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Yang, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wei, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Ganlm: Encoder-decoder pre-training with  an auxiliary discriminator.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' CoRR abs/2212.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='10218 (2022) 2212.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='10218.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2212.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='10218  [51] Liu, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yu, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Peng, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Sun, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Cross-lingual cross-modal con-  solidation for effective multilingual video corpus moment retrieval.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In:  NAACL 2022, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1854–1862 (2022)  [52] Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yin, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ma, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wu, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Guo, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Wei, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': UM4: unified multilingual multiple teacher-student model for  zero-resource neural machine translation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Raedt, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (ed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Pro-  ceedings of the Thirty-First International Joint Conference on Artifi-  cial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  4454–4460.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='24963/ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2022/618.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='24963/ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='2022/618  [53] Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ma, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Huang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Dong, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Huang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Muzio, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Singhal, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Hassan, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Song, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wei, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Multilingual machine transla-  tion systems from microsoft for WMT21 shared task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: WMT@EMNLP  2021, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 446–455 (2021)  [54] Guo, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Huang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wei, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Lvp-  m3: Language-aware visual prompt for multilingual multimodal machine  translation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' EMNLP (2022)  [55] Firat, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Cho, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Bengio, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Multi-way, multilingual neural machine  translation with a shared attention mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Knight, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Nenkova,  A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Rambow, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') NAACL HLT 2016, The 2016 Conference  of the North American Chapter of the Association for Computa-  tional Linguistics: Human Language Technologies, San Diego California,  USA, June 12-17, 2016, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 866–875.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' The Association for Compu-  tational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/n16-1101.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/n16-1101  Springer Nature 2021 LATEX template  Article Title  27  [56] Johnson, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Schuster, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Le, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Krikun, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chen, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tho-  rat, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Vi´egas, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wattenberg, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Corrado, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Hughes, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Dean, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=':  Google’s multilingual neural machine translation system: Enabling zero-  shot translation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Trans.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Assoc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Comput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Linguistics 5, 339–351 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='1162/tacl a 00065  [57] Fan, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Bhosale, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Schwenk, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ma, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', El-Kishky, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Goyal, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Baines, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Celebi, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wenzek, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chaudhary, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Goyal, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Birch, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  Liptchinsky, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Edunov, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Auli, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Joulin, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Beyond english-centric  multilingual machine translation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Mach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Learn.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Res.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 22, 107–110748  (2021)  [58] Lin, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Pan, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wang, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Qiu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Feng, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Zhou, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Li, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Pre-training  multilingual neural machine translation by leveraging alignment informa-  tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Webber, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Cohn, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', He, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the  2020 Conference on Empirical Methods in Natural Language Processing,  EMNLP 2020, Online, November 16-20, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 2649–2663.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association  for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/  2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='emnlp-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='210.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='emnlp-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='210  [59] Aharoni, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Johnson, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Firat, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Massively multilingual neural  machine translation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Burstein, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Doran, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Solorio, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Pro-  ceedings of the 2019 Conference of the North American Chapter of the  Association for Computational Linguistics: Human Language Technolo-  gies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume  1 (Long and Short Papers), pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 3874–3884.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Compu-  tational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/n19-1388.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/n19-1388  [60] Zhang, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Williams, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Titov, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Sennrich, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Improving massively  multilingual neural machine translation and zero-shot translation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In:  Jurafsky, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Chai, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Schluter, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tetreault, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings  of the 58th Annual Meeting of the Association for Computational Lin-  guistics, ACL 2020, Online, July 5-10, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 1628–1639.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association  for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/  2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='acl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='148.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='acl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='148  [61] V´azquez, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Raganato, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Tiedemann, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Creutz, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Multilingual NMT  with a language-independent attention bridge.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Augenstein, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Gella,  S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ruder, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Kann, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Can, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Welbl, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Conneau, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Ren, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Rei, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 4th Workshop on Representation Learning for  NLP, RepL4NLP@ACL 2019, Florence, Italy, August 2, 2019, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 33–39.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Association for Computational Linguistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/  10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/w19-4305.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/w19-4305  [62] Philip, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Berard, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Gall´e, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Besacier, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Monolingual adapters  Springer Nature 2021 LATEX template  28  Article Title  for zero-shot neural machine translation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Webber, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Cohn, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  He, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Liu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Proceedings of the 2020 Conference on Empir-  ical Methods in Natural Language Processing, EMNLP 2020, Online,  November 16-20, 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 4465–4470.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Association for Computational Lin-  guistics, ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='emnlp-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='361.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='emnlp-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='361  [63] Vaswani, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Shazeer, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Parmar, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Uszkoreit, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Jones, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Gomez,  A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Kaiser, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Polosukhin, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Attention is all you need.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In:  Guyon, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Luxburg, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Bengio, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Wallach, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Fergus, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Vish-  wanathan, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Garnett, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=') Advances in Neural Information  Processing Systems, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' 30.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' Curran Associates, Inc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://proceedings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='neurips.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-  Paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='pdf  [64] Shazeer, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Mirhoseini, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Maziarz, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Davis, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Le, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Hinton,  G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=', Dean, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=': Outrageously large neural networks: The sparsely-gated  mixture-of-experts layer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: International Conference on Learning Rep-  resentations (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' https://openreview.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='net/forum?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='id=B1ckMDqlg  [65] K¨oksal,  A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=',  ¨Ozg¨ur,  A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=':  The  RELX  dataset  and  matching  the  multilingual blanks for cross-lingual relation classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content=' In: Find-  ings  of  the  Association  for  Computational  Linguistics:  EMNLP  2020,  pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  340–350.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  Association  for  Computational  Linguistics,  Online  (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='18653/v1/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='findings-emnlp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='  https://www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='aclweb.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='org/anthology/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='findings-emnlp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}
+page_content='32' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-dE3T4oBgHgl3EQfSgnV/content/2301.04434v1.pdf'}