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K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
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Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, Bowen Zhou
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Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. In this paper, we propose K-PLUG, a knowledge-injected pre-trained language model based on the encoder-decoder transformer that can be transferred to both natural language understanding and generation tasks. Specifically, we propose five knowledge-aware self-supervised pre-training objectives to formulate the learning of domain-specific knowledge, including e-commerce domain-specific knowledge-bases, aspects of product entities, categories of product entities, and unique selling propositions of product entities. We verify our method in a diverse range of e-commerce scenarios that require domain-specific knowledge, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue. K-PLUG significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks. Our code is available.
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https://aclanthology.org/2021.findings-emnlp.1
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https://aclanthology.org/2021.findings-emnlp.1.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts
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Yiming Wang, Ximing Li, Xiaotang Zhou, Jihong Ouyang
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Short text nowadays has become a more fashionable form of text data, e.g., Twitter posts, news titles, and product reviews. Extracting semantic topics from short texts plays a significant role in a wide spectrum of NLP applications, and neural topic modeling is now a major tool to achieve it. Motivated by learning more coherent and semantic topics, in this paper we develop a novel neural topic model named Dual Word Graph Topic Model (DWGTM), which extracts topics from simultaneous word co-occurrence and semantic correlation graphs. To be specific, we learn word features from the global word co-occurrence graph, so as to ingest rich word co-occurrence information; we then generate text features with word features, and feed them into an encoder network to get topic proportions per-text; finally, we reconstruct texts and word co-occurrence graph with topical distributions and word features, respectively. Besides, to capture semantics of words, we also apply word features to reconstruct a word semantic correlation graph computed by pre-trained word embeddings. Upon those ideas, we formulate DWGTM in an auto-encoding paradigm and efficiently train it with the spirit of neural variational inference. Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.
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https://aclanthology.org/2021.findings-emnlp.2
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https://aclanthology.org/2021.findings-emnlp.2.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering
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Chenyu You, Nuo Chen, Yuexian Zou
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Spoken question answering (SQA) requires fine-grained understanding of both spoken documents and questions for the optimal answer prediction. In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage. In the self-supervised stage, we propose three auxiliary self-supervised tasks, including utterance restoration, utterance insertion, and question discrimination, and jointly train the model to capture consistency and coherence among speech documents without any additional data or annotations. We then propose to learn noise-invariant utterance representations in a contrastive objective by adopting multiple augmentation strategies, including span deletion and span substitution. Besides, we design a Temporal-Alignment attention to semantically align the speech-text clues in the learned common space and benefit the SQA tasks. By this means, the training schemes can more effectively guide the generation model to predict more proper answers. Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks. Our code will be publicly available after publication.
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https://aclanthology.org/2021.findings-emnlp.3
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https://aclanthology.org/2021.findings-emnlp.3.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Language Clustering for Multilingual Named Entity Recognition
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Kyle Shaffer
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Recent work in multilingual natural language processing has shown progress in various tasks such as natural language inference and joint multilingual translation. Despite success in learning across many languages, challenges arise where multilingual training regimes often boost performance on some languages at the expense of others. For multilingual named entity recognition (NER) we propose a simple technique that groups similar languages together by using embeddings from a pre-trained masked language model, and automatically discovering language clusters in this embedding space. Specifically, we fine-tune an XLM-Roberta model on a language identification task, and use embeddings from this model for clustering. We conduct experiments on 15 diverse languages in the WikiAnn dataset and show our technique largely outperforms three baselines: (1) training a multilingual model jointly on all available languages, (2) training one monolingual model per language, and (3) grouping languages by linguistic family. We also conduct analyses showing meaningful multilingual transfer for low-resource languages (Swahili and Yoruba), despite being automatically grouped with other seemingly disparate languages.
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https://aclanthology.org/2021.findings-emnlp.4
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https://aclanthology.org/2021.findings-emnlp.4.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Neural News Recommendation with Collaborative News Encoding and Structural User Encoding
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Zhiming Mao, Xingshan Zeng, Kam-Fai Wong
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Automatic news recommendation has gained much attention from the academic community and industry. Recent studies reveal that the key to this task lies within the effective representation learning of both news and users. Existing works typically encode news title and content separately while neglecting their semantic interaction, which is inadequate for news text comprehension. Besides, previous models encode user browsing history without leveraging the structural correlation of user browsed news to reflect user interests explicitly. In this work, we propose a news recommendation framework consisting of collaborative news encoding (CNE) and structural user encoding (SUE) to enhance news and user representation learning. CNE equipped with bidirectional LSTMs encodes news title and content collaboratively with cross-selection and cross-attention modules to learn semantic-interactive news representations. SUE utilizes graph convolutional networks to extract cluster-structural features of user history, followed by intra-cluster and inter-cluster attention modules to learn hierarchical user interest representations. Experiment results on the MIND dataset validate the effectiveness of our model to improve the performance of news recommendation.
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https://aclanthology.org/2021.findings-emnlp.5
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https://aclanthology.org/2021.findings-emnlp.5.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data
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Dian Yu, Kai Sun, Dong Yu, Claire Cardie
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{'url': 'https://dataset.org/examqa/', '#text': 'Despite considerable progress, most machine reading comprehension (MRC) tasks still lack sufficient training data to fully exploit powerful deep neural network models with millions of parameters, and it is laborious, expensive, and time-consuming to create large-scale, high-quality MRC data through crowdsourcing. This paper focuses on generating more training data for MRC tasks by leveraging existing question-answering (QA) data. We first collect a large-scale multi-subject multiple-choice QA dataset for Chinese, ExamQA. We next use incomplete, yet relevant snippets returned by a web search engine as the context for each QA instance to convert it into a weakly-labeled MRC instance. To better use the weakly-labeled data to improve a target MRC task, we evaluate and compare several methods and further propose a self-teaching paradigm. Experimental results show that, upon state-of-the-art MRC baselines, we can obtain +5.1% in accuracy on a multiple-choice Chinese MRC dataset, Cˆ3, and +3.8% in exact match on an extractive Chinese MRC dataset, CMRC 2018, demonstrating the usefulness of the generated QA-based weakly-labeled data for different types of MRC tasks as well as the effectiveness of self-teaching. ExamQA will be available at .'}
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https://aclanthology.org/2021.findings-emnlp.6
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https://aclanthology.org/2021.findings-emnlp.6.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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A Web Scale Entity Extraction System
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Xuanting Cai, Quanbin Ma, Jianyu Liu, Pan Li, Qi Zeng, Zhengkan Yang, Pushkar Tripathi
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Understanding the semantic meaning of content on the web through the lens of entities and concepts has many practical advantages. However, when building large-scale entity extraction systems, practitioners are facing unique challenges involving finding the best ways to leverage the scale and variety of data available on internet platforms. We present learnings from our efforts in building an entity extraction system for multiple document types at large scale using multi-modal Transformers. We empirically demonstrate the effectiveness of multi-lingual, multi-task and cross-document type learning. We also discuss the label collection schemes that help to minimize the amount of noise in the collected data.
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https://aclanthology.org/2021.findings-emnlp.7
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https://aclanthology.org/2021.findings-emnlp.7.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Joint Multimedia Event Extraction from Video and Article
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Brian Chen, Xudong Lin, Christopher Thomas, Manling Li, Shoya Yoshida, Lovish Chum, Heng Ji, Shih-Fu Chang
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Visual and textual modalities contribute complementary information about events described in multimedia documents. Videos contain rich dynamics and detailed unfoldings of events, while text describes more high-level and abstract concepts. However, existing event extraction methods either do not handle video or solely target video while ignoring other modalities. In contrast, we propose the first approach to jointly extract events from both video and text articles. We introduce the new task of Video MultiMedia Event Extraction and propose two novel components to build the first system towards this task. First, we propose the first self-supervised cross-modal event coreference model that can determine coreference between video events and text events without any manually annotated pairs. Second, we introduce the first cross-modal transformer architecture, which extracts structured event information from both videos and text documents. We also construct and will publicly release a new benchmark of video-article pairs, consisting of 860 video-article pairs with extensive annotations for evaluating methods on this task. Our experimental results demonstrate the effectiveness of our proposed method on our new benchmark dataset. We achieve 6.0% and 5.8% absolute F-score gain on multimodal event coreference resolution and multimedia event extraction.
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https://aclanthology.org/2021.findings-emnlp.8
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https://aclanthology.org/2021.findings-emnlp.8.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding
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Yuechen Wang, Wengang Zhou, Houqiang Li
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Temporal language grounding (TLG) aims to localize a video segment in an untrimmed video based on a natural language description. To alleviate the expensive cost of manual annotations for temporal boundary labels,we are dedicated to the weakly supervised setting, where only video-level descriptions are provided for training. Most of the existing weakly supervised methods generate a candidate segment set and learn cross-modal alignment through a MIL-based framework. However, the temporal structure of the video as well as the complicated semantics in the sentence are lost during the learning. In this work, we propose a novel candidate-free framework: Fine-grained Semantic Alignment Network (FSAN), for weakly supervised TLG. Instead of view the sentence and candidate moments as a whole, FSAN learns token-by-clip cross-modal semantic alignment by an iterative cross-modal interaction module, generates a fine-grained cross-modal semantic alignment map, and performs grounding directly on top of the map. Extensive experiments are conducted on two widely-used benchmarks: ActivityNet-Captions, and DiDeMo, where our FSAN achieves state-of-the-art performance.
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https://aclanthology.org/2021.findings-emnlp.9
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https://aclanthology.org/2021.findings-emnlp.9.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation
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Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding
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{'url': 'https://github.com/xieyxclack/factual_coco', '#text': 'Despite significant progress has been achieved in text summarization, factual inconsistency in generated summaries still severely limits its practical applications. Among the key factors to ensure factual consistency, a reliable automatic evaluation metric is the first and the most crucial one. However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice. In light of these challenges, we propose a novel metric to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship among the source document, the generated summary, and the language prior. We remove the effect of language prior, which can cause factual inconsistency, from the total causal effect on the generated summary, and provides a simple yet effective way to evaluate consistency without relying on other auxiliary tasks. We conduct a series of experiments on three public abstractive text summarization datasets, and demonstrate the advantages of the proposed metric in both improving the correlation with human judgments and the convenience of usage. The source code is available at .'}
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https://aclanthology.org/2021.findings-emnlp.10
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https://aclanthology.org/2021.findings-emnlp.10.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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Cross-Modal Retrieval Augmentation for Multi-Modal Classification
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Shir Gur, Natalia Neverova, Chris Stauffer, Ser-Nam Lim, Douwe Kiela, Austin Reiter
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Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here, we explore the use of unstructured external knowledge sources of images and their corresponding captions for improving visual question answering (VQA). First, we train a novel alignment model for embedding images and captions in the same space, which achieves substantial improvement in performance on image-caption retrieval w.r.t. similar methods. Second, we show that retrieval-augmented multi-modal transformers using the trained alignment model improve results on VQA over strong baselines. We further conduct extensive experiments to establish the promise of this approach, and examine novel applications for inference time such as hot-swapping indices.
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https://aclanthology.org/2021.findings-emnlp.11
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https://aclanthology.org/2021.findings-emnlp.11.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition
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Zhiwei Yang, Jing Ma, Hechang Chen, Yunke Zhang, Yi Chang
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Nested Named Entity Recognition (NNER) has been extensively studied, aiming to identify all nested entities from potential spans (i.e., one or more continuous tokens). However, recent studies for NNER either focus on tedious tagging schemas or utilize complex structures, which fail to learn effective span representations from the input sentence with highly nested entities. Intuitively, explicit span representations will contribute to NNER due to the rich context information they contain. In this study, we propose a Hierarchical Transformer (HiTRANS) network for the NNER task, which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner. Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network. Then a label prediction layer is designed to recognize nested entities hierarchically, which naturally explores semantic dependencies among different spans. Experiments on GENIA, ACE-2004, ACE-2005 and NNE datasets demonstrate that our proposed method achieves much better performance than the state-of-the-art approaches.
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https://aclanthology.org/2021.findings-emnlp.12
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https://aclanthology.org/2021.findings-emnlp.12.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Improving Embedding-based Large-scale Retrieval via Label Enhancement
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Peiyang Liu, Xi Wang, Sen Wang, Wei Ye, Xiangyu Xi, Shikun Zhang
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Current embedding-based large-scale retrieval models are trained with 0-1 hard label that indicates whether a query is relevant to a document, ignoring rich information of the relevance degree. This paper proposes to improve embedding-based retrieval from the perspective of better characterizing the query-document relevance degree by introducing label enhancement (LE) for the first time. To generate label distribution in the retrieval scenario, we design a novel and effective supervised LE method that incorporates prior knowledge from dynamic term weighting methods into contextual embeddings. Our method significantly outperforms four competitive existing retrieval models and its counterparts equipped with two alternative LE techniques by training models with the generated label distribution as auxiliary supervision information. The superiority can be easily observed on English and Chinese large-scale retrieval tasks under both standard and cold-start settings.
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https://aclanthology.org/2021.findings-emnlp.13
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https://aclanthology.org/2021.findings-emnlp.13.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Improving Privacy Guarantee and Efficiency of Latent Dirichlet Allocation Model Training Under Differential Privacy
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Tao Huang, Hong Chen
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Latent Dirichlet allocation (LDA), a widely used topic model, is often employed as a fundamental tool for text analysis in various applications. However, the training process of the LDA model typically requires massive text corpus data. On one hand, such massive data may expose private information in the training data, thereby incurring significant privacy concerns. On the other hand, the efficiency of the LDA model training may be impacted, since LDA training often needs to handle these massive text corpus data. To address the privacy issues in LDA model training, some recent works have combined LDA training algorithms that are based on collapsed Gibbs sampling (CGS) with differential privacy. Nevertheless, these works usually have a high accumulative privacy budget due to vast iterations in CGS. Moreover, these works always have low efficiency due to handling massive text corpus data. To improve the privacy guarantee and efficiency, we combine a subsampling method with CGS and propose a novel LDA training algorithm with differential privacy, SUB-LDA. We find that subsampling in CGS naturally improves efficiency while amplifying privacy. We propose a novel metric, the efficiency–privacy function, to evaluate improvements of the privacy guarantee and efficiency. Based on a conventional subsampling method, we propose an adaptive subsampling method to improve the model’s utility produced by SUB-LDA when the subsampling ratio is small. We provide a comprehensive analysis of SUB-LDA, and the experiment results validate its efficiency and privacy guarantee improvements.
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https://aclanthology.org/2021.findings-emnlp.14
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https://aclanthology.org/2021.findings-emnlp.14.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Generating Mammography Reports from Multi-view Mammograms with BERT
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Alexander Yalunin, Elena Sokolova, Ilya Burenko, Alexander Ponomarchuk, Olga Puchkova, Dmitriy Umerenkov
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Writing mammography reports can be error-prone and time-consuming for radiologists. In this paper we propose a method to generate mammography reports given four images, corresponding to the four views used in screening mammography. To the best of our knowledge our work represents the first attempt to generate the mammography report using deep-learning. We propose an encoder-decoder model that includes an EfficientNet-based encoder and a Transformer-based decoder. We demonstrate that the Transformer-based attention mechanism can combine visual and semantic information to localize salient regions on the input mammograms and generate a visually interpretable report. The conducted experiments, including an evaluation by a certified radiologist, show the effectiveness of the proposed method.
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https://aclanthology.org/2021.findings-emnlp.15
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https://aclanthology.org/2021.findings-emnlp.15.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Euphemistic Phrase Detection by Masked Language Model
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Wanzheng Zhu, Suma Bhat
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It is a well-known approach for fringe groups and organizations to use euphemisms—ordinary-sounding and innocent-looking words with a secret meaning—to conceal what they are discussing. For instance, drug dealers often use “pot” for marijuana and “avocado” for heroin. From a social media content moderation perspective, though recent advances in NLP have enabled the automatic detection of such single-word euphemisms, no existing work is capable of automatically detecting multi-word euphemisms, such as “blue dream” (marijuana) and “black tar” (heroin). Our paper tackles the problem of euphemistic phrase detection without human effort for the first time, as far as we are aware. We first perform phrase mining on a raw text corpus (e.g., social media posts) to extract quality phrases. Then, we utilize word embedding similarities to select a set of euphemistic phrase candidates. Finally, we rank those candidates by a masked language model—SpanBERT. Compared to strong baselines, we report 20-50% higher detection accuracies using our algorithm for detecting euphemistic phrases.
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https://aclanthology.org/2021.findings-emnlp.16
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https://aclanthology.org/2021.findings-emnlp.16.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable
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Ruiliu Fu, Han Wang, Xuejun Zhang, Jun Zhou, Yonghong Yan
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Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the machine’s reasoning process. We propose Relation Extractor-Reader and Comparator (RERC), a three-stage framework based on complex question decomposition. The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path. In the 2WikiMultiHopQA dataset, our RERC model has achieved the state-of-the-art performance, with a winning joint F1 score of 53.58 on the leaderboard. All indicators of our RERC are close to human performance, with only 1.95 behind the human level in F1 score of support fact. At the same time, the evidence path provided by our RERC framework has excellent readability and faithfulness.
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https://aclanthology.org/2021.findings-emnlp.17
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https://aclanthology.org/2021.findings-emnlp.17.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Segmenting Natural Language Sentences via Lexical Unit Analysis
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Yangming Li, Lemao Liu, Shuming Shi
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The span-based model enjoys great popularity in recent works of sequence segmentation. However, each of these methods suffers from its own defects, such as invalid predictions. In this work, we introduce a unified span-based model, lexical unit analysis (LUA), that addresses all these matters. Segmenting a lexical unit sequence involves two steps. Firstly, we embed every span by using the representations from a pretraining language model. Secondly, we define a score for every segmentation candidate and apply dynamic programming (DP) to extract the candidate with the maximum score. We have conducted extensive experiments on 3 tasks, (e.g., syntactic chunking), across 7 datasets. LUA has established new state-of-the-art performances on 6 of them. We have achieved even better results through incorporating label correlations.
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https://aclanthology.org/2021.findings-emnlp.18
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https://aclanthology.org/2021.findings-emnlp.18.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Dense Hierarchical Retrieval for Open-domain Question Answering
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Ye Liu, Kazuma Hashimoto, Yingbo Zhou, Semih Yavuz, Caiming Xiong, Philip Yu
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Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum inner product search in the retrieval process. However, current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and highly depend on the splitting process. As a consequence, it may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result. In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework which can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage. Specifically, a document-level retriever first identifies relevant documents, among which relevant passages are then retrieved by a passage-level retriever. The ranking of the retrieved passages will be further calibrated by examining the document-level relevance. In addition, hierarchical title structure and two negative sampling strategies (i.e., In-Doc and In-Sec negatives) are investigated. We apply DHR to large-scale open-domain QA datasets. DHR significantly outperforms the original dense passage retriever, and helps an end-to-end QA system outperform the strong baselines on multiple open-domain QA benchmarks.
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https://aclanthology.org/2021.findings-emnlp.19
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https://aclanthology.org/2021.findings-emnlp.19.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Visually Grounded Concept Composition
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Bowen Zhang, Hexiang Hu, Linlu Qiu, Peter Shaw, Fei Sha
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We investigate ways to compose complex concepts in texts from primitive ones while grounding them in images. We propose Concept and Relation Graph (CRG), which builds on top of constituency analysis and consists of recursively combined concepts with predicate functions. Meanwhile, we propose a concept composition neural network called Composer to leverage the CRG for visually grounded concept learning. Specifically, we learn the grounding of both primitive and all composed concepts by aligning them to images and show that learning to compose leads to more robust grounding results, measured in text-to-image matching accuracy. Notably, our model can model grounded concepts forming at both the finer-grained sentence level and the coarser-grained intermediate level (or word-level). Composer leads to pronounced improvement in matching accuracy when the evaluation data has significant compound divergence from the training data.
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https://aclanthology.org/2021.findings-emnlp.20
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https://aclanthology.org/2021.findings-emnlp.20.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding
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Yen-Ling Kuo, Boris Katz, Andrei Barbu
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Humans are remarkably flexible when understanding new sentences that include combinations of concepts they have never encountered before. Recent work has shown that while deep networks can mimic some human language abilities when presented with novel sentences, systematic variation uncovers the limitations in the language-understanding abilities of networks. We demonstrate that these limitations can be overcome by addressing the generalization challenges in the gSCAN dataset, which explicitly measures how well an agent is able to interpret novel linguistic commands grounded in vision, e.g., novel pairings of adjectives and nouns. The key principle we employ is compositionality: that the compositional structure of networks should reflect the compositional structure of the problem domain they address, while allowing other parameters to be learned end-to-end. We build a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains. Crucially, our network has the same state-of-the-art performance as prior work while generalizing its knowledge when prior work does not. Our network also provides a level of interpretability that enables users to inspect what each part of networks learns. Robust grounded language understanding without dramatic failures and without corner cases is critical to building safe and fair robots; we demonstrate the significant role that compositionality can play in achieving that goal.
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https://aclanthology.org/2021.findings-emnlp.21
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https://aclanthology.org/2021.findings-emnlp.21.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages
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Xinyu Lu, Jipeng Qiang, Yun Li, Yunhao Yuan, Yi Zhu
|
The availability of parallel sentence simplification (SS) is scarce for neural SS modelings. We propose an unsupervised method to build SS corpora from large-scale bilingual translation corpora, alleviating the need for SS supervised corpora. Our method is motivated by the following two findings: neural machine translation model usually tends to generate more high-frequency tokens and the difference of text complexity levels exists between the source and target language of a translation corpus. By taking the pair of the source sentences of translation corpus and the translations of their references in a bridge language, we can construct large-scale pseudo parallel SS data. Then, we keep these sentence pairs with a higher complexity difference as SS sentence pairs. The building SS corpora with an unsupervised approach can satisfy the expectations that the aligned sentences preserve the same meanings and have difference in text complexity levels. Experimental results show that SS methods trained by our corpora achieve the state-of-the-art results and significantly outperform the results on English benchmark WikiLarge.
|
https://aclanthology.org/2021.findings-emnlp.22
|
https://aclanthology.org/2021.findings-emnlp.22.pdf
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EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach
|
Junjie Huang, Duyu Tang, Wanjun Zhong, Shuai Lu, Linjun Shou, Ming Gong, Daxin Jiang, Nan Duan
|
{'url': 'https://github.com/Jun-jie-Huang/WhiteningBERT', '#text': 'Producing the embedding of a sentence in anunsupervised way is valuable to natural language matching and retrieval problems in practice. In this work, we conduct a thorough examination of pretrained model based unsupervised sentence embeddings. We study on fourpretrained models and conduct massive experiments on seven datasets regarding sentence semantics. We have three main findings. First, averaging all tokens is better than only using [CLS] vector. Second, combining both topand bottom layers is better than only using toplayers. Lastly, an easy whitening-based vector normalization strategy with less than 10 linesof code consistently boosts the performance. The whole project including codes and data is publicly available at .'}
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https://aclanthology.org/2021.findings-emnlp.23
|
https://aclanthology.org/2021.findings-emnlp.23.pdf
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EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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TWEETSUMM - A Dialog Summarization Dataset for Customer Service
|
Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov
|
In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on real-world customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.
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https://aclanthology.org/2021.findings-emnlp.24
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https://aclanthology.org/2021.findings-emnlp.24.pdf
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EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
|
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Discourse-Based Sentence Splitting
|
Liam Cripwell, Joël Legrand, Claire Gardent
|
Sentence splitting involves the segmentation of a sentence into two or more shorter sentences. It is a key component of sentence simplification, has been shown to help human comprehension and is a useful preprocessing step for NLP tasks such as summarisation and relation extraction. While several methods and datasets have been proposed for developing sentence splitting models, little attention has been paid to how sentence splitting interacts with discourse structure. In this work, we focus on cases where the input text contains a discourse connective, which we refer to as discourse-based sentence splitting. We create synthetic and organic datasets for discourse-based splitting and explore different ways of combining these datasets using different model architectures. We show that pipeline models which use discourse structure to mediate sentence splitting outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical; that large scale synthetic data provides a better basis for learning than smaller scale organic data; and that training on discourse-focused, rather than on general sentence splitting data provides a better basis for discourse splitting.
|
https://aclanthology.org/2021.findings-emnlp.25
|
https://aclanthology.org/2021.findings-emnlp.25.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering
|
Minghan Li, Ming Li, Kun Xiong, Jimmy Lin
|
{'url': 'https://github.com/alexlimh/DPR_MUF', '#text': 'Multi-task dense retrieval models can be used to retrieve documents from a common corpus (e.g., Wikipedia) for different open-domain question-answering (QA) tasks. However, Karpukhin et al. (2020) shows that jointly learning different QA tasks with one dense model is not always beneficial due to corpus inconsistency. For example, SQuAD only focuses on a small set of Wikipedia articles while datasets like NQ and Trivia cover more entries, and joint training on their union can cause performance degradation. To solve this problem, we propose to train individual dense passage retrievers (DPR) for different tasks and aggregate their predictions during test time, where we use uncertainty estimation as weights to indicate how probable a specific query belongs to each expert’s expertise. Our method reaches state-of-the-art performance on 5 benchmark QA datasets, with up to 10% improvement in top-100 accuracy compared to a joint-training multi-task DPR on SQuAD. We also show that our method handles corpus inconsistency better than the joint-training DPR on a mixed subset of different QA datasets. Code and data are available at .'}
|
https://aclanthology.org/2021.findings-emnlp.26
|
https://aclanthology.org/2021.findings-emnlp.26.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States
|
Zhijing Jin, Zeyu Peng, Tejas Vaidhya, Bernhard Schoelkopf, Rada Mihalcea
|
Mining the causes of political decision-making is an active research area in the field of political science. In the past, most studies have focused on long-term policies that are collected over several decades of time, and have primarily relied on surveys as the main source of predictors. However, the recent COVID-19 pandemic has given rise to a new political phenomenon, where political decision-making consists of frequent short-term decisions, all on the same controlled topic—the pandemic. In this paper, we focus on the question of how public opinion influences policy decisions, while controlling for confounders such as COVID-19 case increases or unemployment rates. Using a dataset consisting of Twitter data from the 50 US states, we classify the sentiments toward governors of each state, and conduct controlled studies and comparisons. Based on the compiled samples of sentiments, policies, and confounders, we conduct causal inference to discover trends in political decision-making across different states.
|
https://aclanthology.org/2021.findings-emnlp.27
|
https://aclanthology.org/2021.findings-emnlp.27.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Self-Attention Graph Residual Convolutional Networks for Event Detection with dependency relations
|
Anan Liu, Ning Xu, Haozhe Liu
|
Event detection (ED) task aims to classify events by identifying key event trigger words embedded in a piece of text. Previous research have proved the validity of fusing syntactic dependency relations into Graph Convolutional Networks(GCN). While existing GCN-based methods explore latent node-to-node dependency relations according to a stationary adjacency tensor, an attention-based dynamic tensor, which can pay much attention to the key node like event trigger or its neighboring nodes, has not been developed. Simultaneously, suffering from the phenomenon of graph information vanishing caused by the symmetric adjacency tensor, existing GCN models can not achieve higher overall performance. In this paper, we propose a novel model Self-Attention Graph Residual Convolution Networks (SA-GRCN) to mine node-to-node latent dependency relations via self-attention mechanism and introduce Graph Residual Network (GResNet) to solve graph information vanishing problem. Specifically, a self-attention module is constructed to generate an attention tensor, representing the dependency attention scores of all words in the sentence. Furthermore, a graph residual term is added to the baseline SA-GCN to construct a GResNet. Considering the syntactically connection of the network input, we initialize the raw adjacency tensor without processed by the self-attention module as the residual term. We conduct experiments on the ACE2005 dataset and the results show significant improvement over competitive baseline methods.
|
https://aclanthology.org/2021.findings-emnlp.28
|
https://aclanthology.org/2021.findings-emnlp.28.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Mixup Decoding for Diverse Machine Translation
|
Jicheng Li, Pengzhi Gao, Xuanfu Wu, Yang Feng, Zhongjun He, Hua Wu, Haifeng Wang
|
Diverse machine translation aims at generating various target language translations for a given source language sentence. To leverage the linear relationship in the sentence latent space introduced by the mixup training, we propose a novel method, MixDiversity, to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding. To further improve the faithfulness and diversity of the translations, we propose two simple but effective approaches to select diverse sentence pairs in the training corpus and adjust the interpolation weight for each pair correspondingly. Moreover, by controlling the interpolation weight, our method can achieve the trade-off between faithfulness and diversity without any additional training, which is required in most of the previous methods. Experiments on WMT’16 en-ro, WMT’14 en-de, and WMT’17 zh-en are conducted to show that our method substantially outperforms all previous diverse machine translation methods.
|
https://aclanthology.org/2021.findings-emnlp.29
|
https://aclanthology.org/2021.findings-emnlp.29.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
An Alignment-Agnostic Model for Chinese Text Error Correction
|
Liying Zheng, Yue Deng, Weishun Song, Liang Xu, Jing Xiao
|
This paper investigates how to correct Chinese text errors with types of mistaken, missing and redundant characters, which are common for Chinese native speakers. Most existing models based on detect-correct framework can correct mistaken characters, but cannot handle missing or redundant characters due to inconsistency between model inputs and outputs. Although Seq2Seq-based or sequence tagging methods provide solutions to the three error types and achieved relatively good results in English context, they do not perform well in Chinese context according to our experiments. In our work, we propose a novel alignment-agnostic detect-correct framework that can handle both text aligned and non-aligned situations and can serve as a cold start model when no annotation data are provided. Experimental results on three datasets demonstrate that our method is effective and achieves a better performance than most recent published models.
|
https://aclanthology.org/2021.findings-emnlp.30
|
https://aclanthology.org/2021.findings-emnlp.30.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
|
Gi-Cheon Kang, Junseok Park, Hwaran Lee, Byoung-Tak Zhang, Jin-Hwa Kim
|
{'url': 'https://github.com/gicheonkang/SGLKT-VisDial', '#text': 'Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context. In this paper, we study how to address two fundamental challenges for this task: (1) reasoning over underlying semantic structures among dialog rounds and (2) identifying several appropriate answers to the given question. To address these challenges, we propose a Sparse Graph Learning (SGL) method to formulate visual dialog as a graph structure learning task. SGL infers inherently sparse dialog structures by incorporating binary and score edges and leveraging a new structural loss function. Next, we introduce a Knowledge Transfer (KT) method that extracts the answer predictions from the teacher model and uses them as pseudo labels. We propose KT to remedy the shortcomings of single ground-truth labels, which severely limit the ability of a model to obtain multiple reasonable answers. As a result, our proposed model significantly improves reasoning capability compared to baseline methods and outperforms the state-of-the-art approaches on the VisDial v1.0 dataset. The source code is available at .'}
|
https://aclanthology.org/2021.findings-emnlp.31
|
https://aclanthology.org/2021.findings-emnlp.31.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Exploring Sentence Community for Document-Level Event Extraction
|
Yusheng Huang, Weijia Jia
|
Document-level event extraction is critical to various natural language processing tasks for providing structured information. Existing approaches by sequential modeling neglect the complex logic structures for long texts. In this paper, we leverage the entity interactions and sentence interactions within long documents and transform each document into an undirected unweighted graph by exploiting the relationship between sentences. We introduce the Sentence Community to represent each event as a subgraph. Furthermore, our framework SCDEE maintains the ability to extract multiple events by sentence community detection using graph attention networks and alleviate the role overlapping issue by predicting arguments in terms of roles. Experiments demonstrate that our framework achieves competitive results over state-of-the-art methods on the large-scale document-level event extraction dataset.
|
https://aclanthology.org/2021.findings-emnlp.32
|
https://aclanthology.org/2021.findings-emnlp.32.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems
|
San Kim, Jin Yea Jang, Minyoung Jung, Saim Shin
|
Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult. Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge. For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pre-trained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5. We observed the performance improvement in the open-domain Korean dialogue model even only English knowledge was given. The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.
|
https://aclanthology.org/2021.findings-emnlp.33
|
https://aclanthology.org/2021.findings-emnlp.33.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
|
emnlp_findings_2021
|
|||
WHOSe Heritage: Classification of UNESCO World Heritage Statements of "Outstanding Universal Value” with Soft Labels
|
Nan Bai, Renqian Luo, Pirouz Nourian, Ana Pereira Roders
|
The UNESCO World Heritage List (WHL) includes the exceptionally valuable cultural and natural heritage to be preserved for mankind. Evaluating and justifying the Outstanding Universal Value (OUV) is essential for each site inscribed in the WHL, and yet a complex task, even for experts, since the selection criteria of OUV are not mutually exclusive. Furthermore, manual annotation of heritage values and attributes from multi-source textual data, which is currently dominant in heritage studies, is knowledge-demanding and time-consuming, impeding systematic analysis of such authoritative documents in terms of their implications on heritage management. This study applies state-of-the-art NLP models to build a classifier on a new dataset containing Statements of OUV, seeking an explainable and scalable automation tool to facilitate the nomination, evaluation, research, and monitoring processes of World Heritage sites. Label smoothing is innovatively adapted to improve the model performance by adding prior inter-class relationship knowledge to generate soft labels. The study shows that the best models fine-tuned from BERT and ULMFiT can reach 94.3% top-3 accuracy. A human study with expert evaluation on the model prediction shows that the models are sufficiently generalizable. The study is promising to be further developed and applied in heritage research and practice.
|
https://aclanthology.org/2021.findings-emnlp.34
|
https://aclanthology.org/2021.findings-emnlp.34.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion
|
Jingwen Xu, Jing Zhang, Xirui Ke, Yuxiao Dong, Hong Chen, Cuiping Li, Yongbin Liu
|
Few-shot knowledge graph completion is to infer the unknown facts (i.e., query head-tail entity pairs) of a given relation with only a few observed reference entity pairs. Its general process is to first encode the implicit relation of an entity pair and then match the relation of a query entity pair with the relations of the reference entity pairs. Most existing methods have thus far encoded an entity pair and matched entity pairs by using the direct neighbors of concerned entities. In this paper, we propose the P-INT model for effective few-shot knowledge graph completion. First, P-INT infers and leverages the paths that can expressively encode the relation of two entities. Second, to capture the fine grained matches, P-INT calculates the interactions of paths instead of mix- ing them for each entity pair. Extensive experimental results demonstrate that P-INT out- performs the state-of-the-art baselines by 11.2– 14.2% in terms of Hits@1. Our codes and datasets are online now.
|
https://aclanthology.org/2021.findings-emnlp.35
|
https://aclanthology.org/2021.findings-emnlp.35.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
|
emnlp_findings_2021
|
|||
Cartography Active Learning
|
Mike Zhang, Barbara Plank
|
We propose Cartography Active Learning (CAL), a novel Active Learning (AL) algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling. CAL is inspired by data maps, which were recently proposed to derive insights into dataset quality (Swayamdipta et al., 2020). We compare our method on popular text classification tasks to commonly used AL strategies, which instead rely on post-training behavior. We demonstrate that CAL is competitive to other common AL methods, showing that training dynamics derived from small seed data can be successfully used for AL. We provide insights into our new AL method by analyzing batch-level statistics utilizing the data maps. Our results further show that CAL results in a more data-efficient learning strategy, achieving comparable or better results with considerably less training data.
|
https://aclanthology.org/2021.findings-emnlp.36
|
https://aclanthology.org/2021.findings-emnlp.36.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
|
emnlp_findings_2021
|
|||
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization
|
Akhil Kedia, Sai Chetan Chinthakindi, Wonho Ryu
|
Meta-learning algorithms such as MAML, Reptile, and FOMAML have led to improved performance of several neural models. The primary difference between standard gradient descent and these meta-learning approaches is that they contain as a small component the gradient for maximizing dot-product between gradients of batches, leading to improved generalization. Previous work has shown that aligned gradients are related to generalization, and have also used the Reptile algorithm in a single-task setting to improve generalization. Inspired by these approaches for a single task setting, this paper proposes to use the finite differences first-order algorithm to calculate this gradient from dot-product of gradients, allowing explicit control on the weightage of this component relative to standard gradients. We use this gradient as a regularization technique, leading to more aligned gradients between different batches. By using the finite differences approximation, our approach does not suffer from O(nˆ2) memory usage of naively calculating the Hessian and can be easily applied to large models with large batch sizes. Our approach achieves state-of-the-art performance on the Gigaword dataset, and shows performance improvements on several datasets such as SQuAD-v2.0, Quasar-T, NewsQA and all the SuperGLUE datasets, with a range of models such as BERT, RoBERTa and ELECTRA. Our method also outperforms previous approaches of Reptile and FOMAML when used as a regularization technique, in both single and multi-task settings. Our method is model agnostic, and introduces no extra trainable weights.
|
https://aclanthology.org/2021.findings-emnlp.37
|
https://aclanthology.org/2021.findings-emnlp.37.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
|
emnlp_findings_2021
|
|||
GooAQ: Open Question Answering with Diverse Answer Types
|
Daniel Khashabi, Amos Ng, Tushar Khot, Ashish Sabharwal, Hannaneh Hajishirzi, Chris Callison-Burch
|
{'i': ['questions', 'answers'], '#text': 'While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQ are collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQ are mined from Google’s responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections. We benchmark T5 models on GooAQ and observe that: (a) in line with recent work, LM’s strong performance on GooAQ’s short-answer questions heavily benefit from annotated data; however, (b) their quality in generating coherent and accurate responses for questions requiring long responses (such as ‘how’ and ‘why’ questions) is less reliant on observing annotated data and mainly supported by their pre-training. We release GooAQ to facilitate further research on improving QA with diverse response types.'}
|
https://aclanthology.org/2021.findings-emnlp.38
|
https://aclanthology.org/2021.findings-emnlp.38.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Attention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions
|
Javier Ferrando, Marta R. Costa-jussà
|
This work proposes an extensive analysis of the Transformer architecture in the Neural Machine Translation (NMT) setting. Focusing on the encoder-decoder attention mechanism, we prove that attention weights systematically make alignment errors by relying mainly on uninformative tokens from the source sequence. However, we observe that NMT models assign attention to these tokens to regulate the contribution in the prediction of the two contexts, the source and the prefix of the target sequence. We provide evidence about the influence of wrong alignments on the model behavior, demonstrating that the encoder-decoder attention mechanism is well suited as an interpretability method for NMT. Finally, based on our analysis, we propose methods that largely reduce the word alignment error rate compared to standard induced alignments from attention weights.
|
https://aclanthology.org/2021.findings-emnlp.39
|
https://aclanthology.org/2021.findings-emnlp.39.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
BFClass: A Backdoor-free Text Classification Framework
|
Zichao Li, Dheeraj Mekala, Chengyu Dong, Jingbo Shang
|
Backdoor attack introduces artificial vulnerabilities into the model by poisoning a subset of the training data via injecting triggers and modifying labels. Various trigger design strategies have been explored to attack text classifiers, however, defending such attacks remains an open problem. In this work, we propose BFClass, a novel efficient backdoor-free training framework for text classification. The backbone of BFClass is a pre-trained discriminator that predicts whether each token in the corrupted input was replaced by a masked language model. To identify triggers, we utilize this discriminator to locate the most suspicious token from each training sample and then distill a concise set by considering their association strengths with particular labels. To recognize the poisoned subset, we examine the training samples with these identified triggers as the most suspicious token, and check if removing the trigger will change the poisoned model’s prediction. Extensive experiments demonstrate that BFClass can identify all the triggers, remove 95% poisoned training samples with very limited false alarms, and achieve almost the same performance as the models trained on the benign training data.
|
https://aclanthology.org/2021.findings-emnlp.40
|
https://aclanthology.org/2021.findings-emnlp.40.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models
|
Taeuk Kim, Bowen Li, Sang-goo Lee
|
As it has been unveiled that pre-trained language models (PLMs) are to some extent capable of recognizing syntactic concepts in natural language, much effort has been made to develop a method for extracting complete (binary) parses from PLMs without training separate parsers. We improve upon this paradigm by proposing a novel chart-based method and an effective top-K ensemble technique. Moreover, we demonstrate that we can broaden the scope of application of the approach into multilingual settings. Specifically, we show that by applying our method on multilingual PLMs, it becomes possible to induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, attaining performance superior or comparable to that of unsupervised PCFGs. We also verify that our approach is robust to cross-lingual transfer. Finally, we provide analyses on the inner workings of our method. For instance, we discover universal attention heads which are consistently sensitive to syntactic information irrespective of the input language.
|
https://aclanthology.org/2021.findings-emnlp.41
|
https://aclanthology.org/2021.findings-emnlp.41.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings
|
Kai Wang, Yu Liu, Dan Lin, Michael Sheng
|
Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space. However, the necessity of hyperbolic space in KGE is still questionable, because the calculation based on hyperbolic geometry is much more complicated than Euclidean operations. In this paper, based on the state-of-the-art hyperbolic-based model RotH, we develop two lightweight Euclidean-based models, called RotL and Rot2L. The RotL model simplifies the hyperbolic operations while keeping the flexible normalization effect. Utilizing a novel two-layer stacked transformation and based on RotL, the Rot2L model obtains an improved representation capability, yet costs fewer parameters and calculations than RotH. The experiments on link prediction show that Rot2L achieves the state-of-the-art performance on two widely-used datasets in low-dimensional knowledge graph embeddings. Furthermore, RotL achieves similar performance as RotH but only requires half of the training time.
|
https://aclanthology.org/2021.findings-emnlp.42
|
https://aclanthology.org/2021.findings-emnlp.42.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade
|
Lei Li, Yankai Lin, Deli Chen, Shuhuai Ren, Peng Li, Jie Zhou, Xu Sun
|
Dynamic early exiting aims to accelerate the inference of pre-trained language models (PLMs) by emitting predictions in internal layers without passing through the entire model. In this paper, we empirically analyze the working mechanism of dynamic early exiting and find that it faces a performance bottleneck under high speed-up ratios. On one hand, the PLMs’ representations in shallow layers lack high-level semantic information and thus are not sufficient for accurate predictions. On the other hand, the exiting decisions made by internal classifiers are unreliable, leading to wrongly emitted early predictions. We instead propose a new framework for accelerating the inference of PLMs, CascadeBERT, which dynamically selects proper-sized and complete models in a cascading manner, providing comprehensive representations for predictions. We further devise a difficulty-aware objective, encouraging the model to output the class probability that reflects the real difficulty of each instance for a more reliable cascading mechanism. Experimental results show that CascadeBERT can achieve an overall 15% improvement under 4x speed-up compared with existing dynamic early exiting methods on six classification tasks, yielding more calibrated and accurate predictions.
|
https://aclanthology.org/2021.findings-emnlp.43
|
https://aclanthology.org/2021.findings-emnlp.43.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Semi-supervised Relation Extraction via Incremental Meta Self-Training
|
Xuming Hu, Chenwei Zhang, Fukun Ma, Chenyao Liu, Lijie Wen, Philip S. Yu
|
To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods suffer from the gradual drift problem, where noisy pseudo labels on unlabeled data are incorporated during training. To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates accurate quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. To reduce the influence of noisy pseudo labels, MetaSRE adopts a pseudo label selection and exploitation scheme which assesses pseudo label quality on unlabeled samples and only exploits high-quality pseudo labels in a self-training fashion to incrementally augment labeled samples for both robustness and accuracy. Experimental results on two public datasets demonstrate the effectiveness of the proposed approach.
|
https://aclanthology.org/2021.findings-emnlp.44
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https://aclanthology.org/2021.findings-emnlp.44.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning
|
Yichao Luo, Yige Xu, Jiacheng Ye, Xipeng Qiu, Qi Zhang
|
{'tex-math': ['F_1@5', 'F_1@M', 'F_1', 'F_1'], 'url': 'https://github.com/xuyige/FGRL4KG', '#text': 'Aiming to generate a set of keyphrases, Keyphrase Generation (KG) is a classical task for capturing the central idea from a given document. Based on Seq2Seq models, the previous reinforcement learning framework on KG tasks utilizes the evaluation metrics to further improve the well-trained neural models. However, these KG evaluation metrics such as and are only aware of the exact correctness of predictions on phrase-level and ignore the semantic similarities between similar predictions and targets, which inhibits the model from learning deep linguistic patterns. In response to this problem, we propose a new fine-grained evaluation metric to improve the RL framework, which considers different granularities: token-level score, edit distance, duplication, and prediction quantities. On the whole, the new framework includes two reward functions: the fine-grained evaluation score and the vanilla score. This framework helps the model identifying some partial match phrases which can be further optimized as the exact match ones. Experiments on KG benchmarks show that our proposed training framework outperforms the previous RL training frameworks among all evaluation scores. In addition, our method can effectively ease the synonym problem and generate a higher quality prediction. The source code is available at .'}
|
https://aclanthology.org/2021.findings-emnlp.45
|
https://aclanthology.org/2021.findings-emnlp.45.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation
|
Jinfa Yang, Yongjie Shi, Xin Tong, Robin Wang, Taiyan Chen, Xianghua Ying
|
To find a suitable embedding for a knowledge graph remains a big challenge nowadays. By using previous knowledge graph embedding methods, every entity in a knowledge graph is usually represented as a k-dimensional vector. As we know, an affine transformation can be expressed in the form of a matrix multiplication followed by a translation vector. In this paper, we firstly utilize a set of affine transformations related to each relation to operate on entity vectors, and then these transformed vectors are used for performing embedding with previous methods. The main advantage of using affine transformations is their good geometry properties with interpretability. Our experimental results demonstrate that the proposed intuitive design with affine transformations provides a statistically significant increase in performance with adding a few extra processing steps or adding a limited number of additional variables. Taking TransE as an example, we employ the scale transformation (the special case of an affine transformation), and only introduce k additional variables for each relation. Surprisingly, it even outperforms RotatE to some extent on various data sets. We also introduce affine transformations into RotatE, Distmult and ComplEx, respectively, and each one outperforms its original method.
|
https://aclanthology.org/2021.findings-emnlp.46
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https://aclanthology.org/2021.findings-emnlp.46.pdf
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EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Using Question Answering Rewards to Improve Abstractive Summarization
|
Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Ranit Aharonov, Sachindra Joshi
|
Neural abstractive summarization models have drastically improved in the recent years. However, the summaries generated by these models generally suffer from issues such as: not capturing the critical facts in source documents, and containing facts that are inconsistent with the source documents. In this work, we present a general framework to train abstractive summarization models to alleviate such issues. We first train a sequence-to-sequence model to summarize documents, and then further train this model in a Reinforcement Learning setting with question-answering based rewards. We evaluate the summaries generated by the this framework using multiple automatic measures and human judgements. The experimental results show that the question-answering rewards can be used as a general framework to improve neural abstractive summarization. Particularly, the results from human evaluations show that the summaries generated by our approach is preferred over 30% of the time over the summaries generated by general abstractive summarization models.
|
https://aclanthology.org/2021.findings-emnlp.47
|
https://aclanthology.org/2021.findings-emnlp.47.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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Effect Generation Based on Causal Reasoning
|
Feiteng Mu, Wenjie Li, Zhipeng Xie
|
Causal reasoning aims to predict the future scenarios that may be caused by the observed actions. However, existing causal reasoning methods deal with causalities on the word level. In this paper, we propose a novel event-level causal reasoning method and demonstrate its use in the task of effect generation. In particular, we structuralize the observed cause-effect event pairs into an event causality network, which describes causality dependencies. Given an input cause sentence, a causal subgraph is retrieved from the event causality network and is encoded with the graph attention mechanism, in order to support better reasoning of the potential effects. The most probable effect event is then selected from the causal subgraph and is used as guidance to generate an effect sentence. Experiments show that our method generates more reasonable effect sentences than various well-designed competitors.
|
https://aclanthology.org/2021.findings-emnlp.48
|
https://aclanthology.org/2021.findings-emnlp.48.pdf
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EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Distilling Word Meaning in Context from Pre-trained Language Models
|
Yuki Arase, Tomoyuki Kajiwara
|
In this study, we propose a self-supervised learning method that distils representations of word meaning in context from a pre-trained masked language model. Word representations are the basis for context-aware lexical semantics and unsupervised semantic textual similarity (STS) estimation. A previous study transforms contextualised representations employing static word embeddings to weaken excessive effects of contextual information. In contrast, the proposed method derives representations of word meaning in context while preserving useful context information intact. Specifically, our method learns to combine outputs of different hidden layers using self-attention through self-supervised learning with an automatically generated training corpus. To evaluate the performance of the proposed approach, we performed comparative experiments using a range of benchmark tasks. The results confirm that our representations exhibited a competitive performance compared to that of the state-of-the-art method transforming contextualised representations for the context-aware lexical semantic tasks and outperformed it for STS estimation.
|
https://aclanthology.org/2021.findings-emnlp.49
|
https://aclanthology.org/2021.findings-emnlp.49.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation
|
Xin Huang, Jung-Jae Kim, Bowei Zou
|
Complex question answering over knowledge base remains as a challenging task because it involves reasoning over multiple pieces of information, including intermediate entities/relations and other constraints. Previous methods simplify the SPARQL query of a question into such forms as a list or a graph, missing such constraints as “filter” and “order_by”, and present models specialized for generating those simplified forms from a given question. We instead introduce a novel approach that directly generates an executable SPARQL query without simplification, addressing the issue of generating unseen entities. We adapt large scale pre-trained encoder-decoder models and show that our method significantly outperforms the previous methods and also that our method has higher interpretability and computational efficiency than the previous methods.
|
https://aclanthology.org/2021.findings-emnlp.50
|
https://aclanthology.org/2021.findings-emnlp.50.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction
|
Guimin Hu, Guangming Lu, Yi Zhao
|
Emotion cause extraction (ECE) aims to extract the causes behind the certain emotion in text. Some works related to the ECE task have been published and attracted lots of attention in recent years. However, these methods neglect two major issues: 1) pay few attentions to the effect of document-level context information on ECE, and 2) lack of sufficient exploration for how to effectively use the annotated emotion clause. For the first issue, we propose a bidirectional hierarchical attention network (BHA) corresponding to the specified candidate cause clause to capture the document-level context in a structured and dynamic manner. For the second issue, we design an emotional filtering module (EF) for each layer of the graph attention network, which calculates a gate score based on the emotion clause to filter the irrelevant information. Combining the BHA and EF, the EF-BHA can dynamically aggregate the contextual information from two directions and filters irrelevant information. The experimental results demonstrate that EF-BHA achieves the competitive performances on two public datasets in different languages (Chinese and English). Moreover, we quantify the effect of context on emotion cause extraction and provide the visualization of the interactions between candidate cause clauses and contexts.
|
https://aclanthology.org/2021.findings-emnlp.51
|
https://aclanthology.org/2021.findings-emnlp.51.pdf
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EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Distantly Supervised Relation Extraction in Federated Settings
|
Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao
|
In relation extraction, distant supervision is widely used to automatically label a large-scale training dataset by aligning a knowledge base with unstructured text. Most existing studies in this field have assumed there is a great deal of centralized unstructured text. However, in practice, texts are usually distributed on different platforms and cannot be centralized due to privacy restrictions. Therefore, it is worthwhile to investigate distant supervision in the federated learning paradigm, which decouples the training of the model from the need for direct access to raw texts. However, overcoming label noise of distant supervision becomes more difficult in federated settings, because texts containing the same entity pair scatter around different platforms. In this paper, we propose a federated denoising framework to suppress label noise in federated settings. The key of this framework is a multiple instance learning based denoising method that is able to select reliable sentences via cross-platform collaboration. Various experiments on New York Times dataset and miRNA gene regulation relation dataset demonstrate the effectiveness of the proposed method.
|
https://aclanthology.org/2021.findings-emnlp.52
|
https://aclanthology.org/2021.findings-emnlp.52.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Casting the Same Sentiment Classification Problem
|
Erik Körner, Ahmad Dawar Hakimi, Gerhard Heyer, Martin Potthast
|
We introduce and study a problem variant of sentiment analysis, namely the “same sentiment classification problem”, where, given a pair of texts, the task is to determine if they have the same sentiment, disregarding the actual sentiment polarity. Among other things, our goal is to enable a more topic-agnostic sentiment classification. We study the problem using the Yelp business review dataset, demonstrating how sentiment data needs to be prepared for this task, and then carry out sequence pair classification using the BERT language model. In a series of experiments, we achieve an accuracy above 83% for category subsets across topics, and 89% on average.
|
https://aclanthology.org/2021.findings-emnlp.53
|
https://aclanthology.org/2021.findings-emnlp.53.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing
|
Denis Lukovnikov, Sina Daubener, Asja Fischer
|
While neural networks are ubiquitous in state-of-the-art semantic parsers, it has been shown that most standard models suffer from dramatic performance losses when faced with compositionally out-of-distribution (OOD) data. Recently several methods have been proposed to improve compositional generalization in semantic parsing. In this work we instead focus on the problem of detecting compositionally OOD examples with neural semantic parsers, which, to the best of our knowledge, has not been investigated before. We investigate several strong yet simple methods for OOD detection based on predictive uncertainty. The experimental results demonstrate that these techniques perform well on the standard SCAN and CFQ datasets. Moreover, we show that OOD detection can be further improved by using a heterogeneous ensemble.
|
https://aclanthology.org/2021.findings-emnlp.54
|
https://aclanthology.org/2021.findings-emnlp.54.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification
|
Siyu Lai, Hui Huang, Dong Jing, Yufeng Chen, Jinan Xu, Jian Liu
|
Recent multilingual pre-trained models, like XLM-RoBERTa (XLM-R), have been demonstrated effective in many cross-lingual tasks. However, there are still gaps between the contextualized representations of similar words in different languages. To solve this problem, we propose a novel framework named Multi-View Mixed Language Training (MVMLT), which leverages code-switched data with multi-view learning to fine-tune XLM-R. MVMLT uses gradient-based saliency to extract keywords which are the most relevant to downstream tasks and replaces them with the corresponding words in the target language dynamically. Furthermore, MVMLT utilizes multi-view learning to encourage contextualized embeddings to align into a more refined language-invariant space. Extensive experiments with four languages show that our model achieves state-of-the-art results on zero-shot cross-lingual sentiment classification and dialogue state tracking tasks, demonstrating the effectiveness of our proposed model.
|
https://aclanthology.org/2021.findings-emnlp.55
|
https://aclanthology.org/2021.findings-emnlp.55.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
|
Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
|
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.
|
https://aclanthology.org/2021.findings-emnlp.56
|
https://aclanthology.org/2021.findings-emnlp.56.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
FANATIC: FAst Noise-Aware TopIc Clustering
|
Ari Silburt, Anja Subasic, Evan Thompson, Carmeline Dsilva, Tarec Fares
|
Extracting salient topics from a collection of documents can be a challenging task when a) the amount of data is large, b) the number of topics is not known a priori, and/or c) “topic noise” is present. We define “topic noise” as the collection of documents that are irrelevant to any coherent topic and should be filtered out. By design, most clustering algorithms (e.g. k-means, hierarchical clustering) assign all input documents to one of the available clusters, guaranteeing any topic noise to propagate into the result. To address these challenges, we present a novel algorithm, FANATIC, that efficiently distinguishes documents from genuine topics and those that are topic noise. We also introduce a new Reddit dataset to showcase FANATIC as it contains short, noisy data that is difficult to cluster using most clustering algorithms. We find that FANATIC clusters 500k Reddit titles (of which 20% are topic noise) in 2 minutes and achieves an AMI score of 0.59, in contrast with hdbscan (McInnes et al., 2017), a popular algorithm suited for this type of task, which requires over 7 hours and achieves an AMI of 0.03. Finally, we test FANATIC against a Twitter dataset and find again that it outperforms the other algorithms with an AMI score of 0.60. We make our code and data publicly available.
|
https://aclanthology.org/2021.findings-emnlp.57
|
https://aclanthology.org/2021.findings-emnlp.57.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Stream-level Latency Evaluation for Simultaneous Machine Translation
|
Javier Iranzo-Sánchez, Jorge Civera Saiz, Alfons Juan
|
Simultaneous machine translation has recently gained traction thanks to significant quality improvements and the advent of streaming applications. Simultaneous translation systems need to find a trade-off between translation quality and response time, and with this purpose multiple latency measures have been proposed. However, latency evaluations for simultaneous translation are estimated at the sentence level, not taking into account the sequential nature of a streaming scenario. Indeed, these sentence-level latency measures are not well suited for continuous stream translation, resulting in figures that are not coherent with the simultaneous translation policy of the system being assessed. This work proposes a stream level adaptation of the current latency measures based on a re-segmentation approach applied to the output translation, that is successfully evaluated on streaming conditions for a reference IWSLT task.
|
https://aclanthology.org/2021.findings-emnlp.58
|
https://aclanthology.org/2021.findings-emnlp.58.pdf
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EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning
|
Kexin Wang, Nils Reimers, Iryna Gurevych
|
Learning sentence embeddings often requires a large amount of labeled data. However, for most tasks and domains, labeled data is seldom available and creating it is expensive. In this work, we present a new state-of-the-art unsupervised method based on pre-trained Transformers and Sequential Denoising Auto-Encoder (TSDAE) which outperforms previous approaches by up to 6.4 points. It can achieve up to 93.1% of the performance of in-domain supervised approaches. Further, we show that TSDAE is a strong domain adaptation and pre-training method for sentence embeddings, significantly outperforming other approaches like Masked Language Model. A crucial shortcoming of previous studies is the narrow evaluation: Most work mainly evaluates on the single task of Semantic Textual Similarity (STS), which does not require any domain knowledge. It is unclear if these proposed methods generalize to other domains and tasks. We fill this gap and evaluate TSDAE and other recent approaches on four different datasets from heterogeneous domains.
|
https://aclanthology.org/2021.findings-emnlp.59
|
https://aclanthology.org/2021.findings-emnlp.59.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?
|
Chantal Amrhein, Rico Sennrich
|
Data-driven subword segmentation has become the default strategy for open-vocabulary machine translation and other NLP tasks, but may not be sufficiently generic for optimal learning of non-concatenative morphology. We design a test suite to evaluate segmentation strategies on different types of morphological phenomena in a controlled, semi-synthetic setting. In our experiments, we compare how well machine translation models trained on subword- and character-level can translate these morphological phenomena. We find that learning to analyse and generate morphologically complex surface representations is still challenging, especially for non-concatenative morphological phenomena like reduplication or vowel harmony and for rare word stems. Based on our results, we recommend that novel text representation strategies be tested on a range of typologically diverse languages to minimise the risk of adopting a strategy that inadvertently disadvantages certain languages.
|
https://aclanthology.org/2021.findings-emnlp.60
|
https://aclanthology.org/2021.findings-emnlp.60.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Rethinking Why Intermediate-Task Fine-Tuning Works
|
Ting-Yun Chang, Chi-Jen Lu
|
Supplementary Training on Intermediate Labeled-data Tasks (STILT) is a widely applied technique, which first fine-tunes the pretrained language models on an intermediate task before on the target task of interest. While STILT is able to further improve the performance of pretrained language models, it is still unclear why and when it works. Previous research shows that those intermediate tasks involving complex inference, such as commonsense reasoning, work especially well for RoBERTa-large. In this paper, we discover that the improvement from an intermediate task could be orthogonal to it containing reasoning or other complex skills — a simple real-fake discrimination task synthesized by GPT2 can benefit diverse target tasks. We conduct extensive experiments to study the impact of different factors on STILT. These findings suggest rethinking the role of intermediate fine-tuning in the STILT pipeline.
|
https://aclanthology.org/2021.findings-emnlp.61
|
https://aclanthology.org/2021.findings-emnlp.61.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning
|
Xisen Jin, Bill Yuchen Lin, Mohammad Rostami, Xiang Ren
|
The ability to continuously expand knowledge over time and utilize it to rapidly generalize to new tasks is a key feature of human linguistic intelligence. Existing models that pursue rapid generalization to new tasks (e.g., few-shot learning methods), however, are mostly trained in a single shot on fixed datasets, unable to dynamically expand their knowledge; while continual learning algorithms are not specifically designed for rapid generalization. We present a new learning setup, Continual Learning of Few-Shot Learners (CLIF), to address challenges of both learning settings in a unified setup. CLIF assumes a model learns from a sequence of diverse NLP tasks arriving sequentially, accumulating knowledge for improved generalization to new tasks, while also retaining performance on the tasks learned earlier. We examine how the generalization ability is affected in the continual learning setup, evaluate a number of continual learning algorithms, and propose a novel regularized adapter generation approach. We find that catastrophic forgetting affects generalization ability to a lesser degree than performance on seen tasks; while continual learning algorithms can still bring considerable benefit to the generalization ability.
|
https://aclanthology.org/2021.findings-emnlp.62
|
https://aclanthology.org/2021.findings-emnlp.62.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Efficient Test Time Adapter Ensembling for Low-resource Language Varieties
|
Xinyi Wang, Yulia Tsvetkov, Sebastian Ruder, Graham Neubig
|
Adapters are light-weight modules that allow parameter-efficient fine-tuning of pretrained models. Specialized language and task adapters have recently been proposed to facilitate cross-lingual transfer of multilingual pretrained models (Pfeiffer et al., 2020b). However, this approach requires training a separate language adapter for every language one wishes to support, which can be impractical for languages with limited data. An intuitive solution is to use a related language adapter for the new language variety, but we observe that this solution can lead to sub-optimal performance. In this paper, we aim to improve the robustness of language adapters to uncovered languages without training new adapters. We find that ensembling multiple existing language adapters makes the fine-tuned model significantly more robust to other language varieties not included in these adapters. Building upon this observation, we propose Entropy Minimized Ensemble of Adapters (EMEA), a method that optimizes the ensemble weights of the pretrained language adapters for each test sentence by minimizing the entropy of its predictions. Experiments on three diverse groups of language varieties show that our method leads to significant improvements on both named entity recognition and part-of-speech tagging across all languages.
|
https://aclanthology.org/2021.findings-emnlp.63
|
https://aclanthology.org/2021.findings-emnlp.63.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces
|
Kelly Marchisio, Youngser Park, Ali Saad-Eldin, Anton Alyakin, Kevin Duh, Carey Priebe, Philipp Koehn
|
{'url': 'https://github.com/kellymarchisio/euc-v-graph-bli', '#text': 'Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may be understood as nodes in a weighted graph. This framing allows us to examine a node’s graph neighborhood without assuming a linear transform, and exploits new techniques from the graph matching optimization literature. These contrasting approaches have not been compared in BLI so far. In this work, we study the behavior of Euclidean versus graph-based approaches to BLI under differing data conditions and show that they complement each other when combined. We release our code at .'}
|
https://aclanthology.org/2021.findings-emnlp.64
|
https://aclanthology.org/2021.findings-emnlp.64.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding
|
Tianda Li, Ahmad Rashid, Aref Jafari, Pranav Sharma, Ali Ghodsi, Mehdi Rezagholizadeh
|
Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge in a large neural network into a smaller one. Even though KD has shown promise on a wide range of Natural Language Processing (NLP) applications, little is understood about how one KD algorithm compares to another and whether these approaches can be complimentary to each other. In this work, we evaluate various KD algorithms on in-domain, out-of-domain and adversarial testing. We propose a framework to assess adversarial robustness of multiple KD algorithms. Moreover, we introduce a new KD algorithm, Combined-KD, which takes advantage of two promising approaches (better training scheme and more efficient data augmentation). Our extensive experimental results show that Combined-KD achieves state-of-the-art results on the GLUE benchmark, out-of-domain generalization, and adversarial robustness compared to competitive methods.
|
https://aclanthology.org/2021.findings-emnlp.65
|
https://aclanthology.org/2021.findings-emnlp.65.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction
|
Zeyu Li, Wei Cheng, Reema Kshetramade, John Houser, Haifeng Chen, Wei Wang
|
Compliments and concerns in reviews are valuable for understanding users’ shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user-attention and item-property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidences. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.
|
https://aclanthology.org/2021.findings-emnlp.66
|
https://aclanthology.org/2021.findings-emnlp.66.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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Learning Hard Retrieval Decoder Attention for Transformers
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Hongfei Xu, Qiuhui Liu, Josef van Genabith, Deyi Xiong
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The Transformer translation model is based on the multi-head attention mechanism, which can be parallelized easily. The multi-head attention network performs the scaled dot-product attention function in parallel, empowering the model by jointly attending to information from different representation subspaces at different positions. In this paper, we present an approach to learning a hard retrieval attention where an attention head only attends to one token in the sentence rather than all tokens. The matrix multiplication between attention probabilities and the value sequence in the standard scaled dot-product attention can thus be replaced by a simple and efficient retrieval operation. We show that our hard retrieval attention mechanism is 1.43 times faster in decoding, while preserving translation quality on a wide range of machine translation tasks when used in the decoder self- and cross-attention networks.
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https://aclanthology.org/2021.findings-emnlp.67
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https://aclanthology.org/2021.findings-emnlp.67.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Recall and Learn: A Memory-augmented Solver for Math Word Problems
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Shifeng Huang, Jiawei Wang, Jiao Xu, Da Cao, Ming Yang
|
In this article, we tackle the math word problem, namely, automatically answering a mathematical problem according to its textual description. Although recent methods have demonstrated their promising results, most of these methods are based on template-based generation scheme which results in limited generalization capability. To this end, we propose a novel human-like analogical learning method in a recall and learn manner. Our proposed framework is composed of modules of memory, representation, analogy, and reasoning, which are designed to make a new exercise by referring to the exercises learned in the past. Specifically, given a math word problem, the model first retrieves similar questions by a memory module and then encodes the unsolved problem and each retrieved question using a representation module. Moreover, to solve the problem in a way of analogy, an analogy module and a reasoning module with a copy mechanism are proposed to model the interrelationship between the problem and each retrieved question. Extensive experiments on two well-known datasets show the superiority of our proposed algorithm as compared to other state-of-the-art competitors from both overall performance comparison and micro-scope studies.
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https://aclanthology.org/2021.findings-emnlp.68
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https://aclanthology.org/2021.findings-emnlp.68.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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An Uncertainty-Aware Encoder for Aspect Detection
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Thi-Nhung Nguyen, Kiem-Hieu Nguyen, Young-In Song, Tuan-Dung Cao
|
Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classifiers. This paper presents a novel weakly-supervised method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.
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https://aclanthology.org/2021.findings-emnlp.69
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https://aclanthology.org/2021.findings-emnlp.69.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations
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Jun Gao, Yuhan Liu, Haolin Deng, Wei Wang, Yu Cao, Jiachen Du, Ruifeng Xu
|
Current approaches to empathetic response generation focus on learning a model to predict an emotion label and generate a response based on this label and have achieved promising results. However, the emotion cause, an essential factor for empathetic responding, is ignored. The emotion cause is a stimulus for human emotions. Recognizing the emotion cause is helpful to better understand human emotions so as to generate more empathetic responses. To this end, we propose a novel framework that improves empathetic response generation by recognizing emotion cause in conversations. Specifically, an emotion reasoner is designed to predict a context emotion label and a sequence of emotion cause-oriented labels, which indicate whether the word is related to the emotion cause. Then we devise both hard and soft gated attention mechanisms to incorporate the emotion cause into response generation. Experiments show that incorporating emotion cause information improves the performance of the model on both emotion recognition and response generation.
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https://aclanthology.org/2021.findings-emnlp.70
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https://aclanthology.org/2021.findings-emnlp.70.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Probing Across Time: What Does RoBERTa Know and When?
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Zeyu Liu, Yizhong Wang, Jungo Kasai, Hannaneh Hajishirzi, Noah A. Smith
|
Models of language trained on very large corpora have been demonstrated useful for natural language processing. As fixed artifacts, they have become the object of intense study, with many researchers “probing” the extent to which they acquire and readily demonstrate linguistic abstractions, factual and commonsense knowledge, and reasoning abilities. Recent work applied several probes to intermediate training stages to observe the developmental process of a large-scale model (Chiang et al., 2020). Following this effort, we systematically answer a question: for various types of knowledge a language model learns, when during (pre)training are they acquired? Using RoBERTa as a case study, we find: linguistic knowledge is acquired fast, stably, and robustly across domains. Facts and commonsense are slower and more domain-sensitive. Reasoning abilities are, in general, not stably acquired. As new datasets, pretraining protocols, and probes emerge, we believe that probing-across-time analyses can help researchers understand the complex, intermingled learning that these models undergo and guide us toward more efficient approaches that accomplish necessary learning faster.
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https://aclanthology.org/2021.findings-emnlp.71
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https://aclanthology.org/2021.findings-emnlp.71.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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Knowledge-Guided Paraphrase Identification
|
Haoyu Wang, Fenglong Ma, Yaqing Wang, Jing Gao
|
Paraphrase identification (PI), a fundamental task in natural language processing, is to identify whether two sentences express the same or similar meaning, which is a binary classification problem. Recently, BERT-like pre-trained language models have been a popular choice for the frameworks of various PI models, but almost all existing methods consider general domain text. When these approaches are applied to a specific domain, existing models cannot make accurate predictions due to the lack of professional knowledge. In light of this challenge, we propose a novel framework, namely , which can leverage the external unstructured Wikipedia knowledge to accurately identify paraphrases. We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model. After retrieving related outline knowledge, makes predictions based on both the semantic information of two sentences and the outline knowledge. Besides, we propose a gating mechanism to aggregate the semantic information-based prediction and the knowledge-based prediction. Extensive experiments are conducted on two public datasets: PARADE (a computer science domain dataset) and clinicalSTS2019 (a biomedical domain dataset). The results show that the proposed outperforms state-of-the-art methods.
|
https://aclanthology.org/2021.findings-emnlp.72
|
https://aclanthology.org/2021.findings-emnlp.72.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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R2-D2: A Modular Baseline for Open-Domain Question Answering
|
Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz
|
This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). The pipeline is composed of a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system’s components. We demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA, surpassing state-of-the-art on the first two. Our analysis demonstrates that: (i) combining extractive and generative reader yields absolute improvements up to 5 exact match and it is at least twice as effective as the posterior averaging ensemble of the same models with different parameters, (ii) the extractive reader with fewer parameters can match the performance of the generative reader on extractive QA datasets.
|
https://aclanthology.org/2021.findings-emnlp.73
|
https://aclanthology.org/2021.findings-emnlp.73.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability
|
Yaochen Liu, Yazhou Zhang, Qiuchi Li, Benyou Wang, Dawei Song
|
Sarcasm and sentiment embody intrinsic uncertainty of human cognition, making joint detection of multi-modal sarcasm and sentiment a challenging task. In view of the advantages of quantum probability (QP) in modeling such uncertainty, this paper explores the potential of QP as a mathematical framework and proposes a QP driven multi-task (QPM) learning framework. The QPM framework involves a complex-valued multi-modal representation encoder, a quantum-like fusion subnetwork and a quantum measurement mechanism. Each multi-modal (e.g., textual, visual) utterance is first encoded as a quantum superposition of a set of basis terms using a complex-valued representation. Then, the quantum-like fusion subnetwork leverages quantum state composition and quantum interference to model the contextual interaction between adjacent utterances and the correlations across modalities respectively. Finally, quantum incompatible measurements are performed on the multi-modal representation of each utterance to yield the probabilistic outcomes of sarcasm and sentiment recognition. The experimental results show that our model achieves a state-of-the-art performance.
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https://aclanthology.org/2021.findings-emnlp.74
|
https://aclanthology.org/2021.findings-emnlp.74.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Discovering Representation Sprachbund For Multilingual Pre-Training
|
Yimin Fan, Yaobo Liang, Alexandre Muzio, Hany Hassan, Houqiang Li, Ming Zhou, Nan Duan
|
Multilingual pre-trained models have demonstrated their effectiveness in many multilingual NLP tasks and enabled zero-shot or few-shot transfer from high-resource languages to low-resource ones. However, due to significant typological differences and contradictions between some languages, such models usually perform poorly on many languages and cross-lingual settings, which shows the difficulty of learning a single model to handle massive diverse languages well at the same time. To alleviate this issue, we present a new multilingual pre-training pipeline. We propose to generate language representation from multilingual pre-trained model and conduct linguistic analysis to show that language representation similarity reflects linguistic similarity from multiple perspectives, including language family, geographical sprachbund, lexicostatistics, and syntax. Then we cluster all the target languages into multiple groups and name each group as a representation sprachbund. Thus, languages in the same representation sprachbund are supposed to boost each other in both pre-training and fine-tuning as they share rich linguistic similarity. We pre-train one multilingual model for each representation sprachbund. Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
|
https://aclanthology.org/2021.findings-emnlp.75
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https://aclanthology.org/2021.findings-emnlp.75.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Plan-then-Generate: Controlled Data-to-Text Generation via Planning
|
Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, Nigel Collier
|
Recent developments in neural networks have led to the advance in data-to-text generation. However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications. In this study, we propose a novel Plan-then-Generate (PlanGen) framework to improve the controllability of neural data-to-text models. Extensive experiments and analyses are conducted on two benchmark datasets, ToTTo and WebNLG. The results show that our model is able to control both the intra-sentence and inter-sentence structure of the generated output. Furthermore, empirical comparisons against previous state-of-the-art methods show that our model improves the generation quality as well as the output diversity as judged by human and automatic evaluations.
|
https://aclanthology.org/2021.findings-emnlp.76
|
https://aclanthology.org/2021.findings-emnlp.76.pdf
|
EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Few-Shot Table-to-Text Generation with Prototype Memory
|
Yixuan Su, Zaiqiao Meng, Simon Baker, Nigel Collier
|
Neural table-to-text generation models have achieved remarkable progress on an array of tasks. However, due to the data-hungry nature of neural models, their performances strongly rely on large-scale training examples, limiting their applicability in real-world applications. To address this, we propose a new framework: Prototype-to-Generate (P2G), for table-to-text generation under the few-shot scenario. The proposed framework utilizes the retrieved prototypes, which are jointly selected by an IR system and a novel prototype selector to help the model bridging the structural gap between tables and texts. Experimental results on three benchmark datasets with three state-of-the-art models demonstrate that the proposed framework significantly improves the model performance across various evaluation metrics.
|
https://aclanthology.org/2021.findings-emnlp.77
|
https://aclanthology.org/2021.findings-emnlp.77.pdf
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EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation
|
Hua Zheng, Lei Li, Damai Dai, Deli Chen, Tianyu Liu, Xu Sun, Yang Liu
|
In parataxis languages like Chinese, word meanings are constructed using specific word-formations, which can help to disambiguate word senses. However, such knowledge is rarely explored in previous word sense disambiguation (WSD) methods. In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD. We first construct a large-scale Chinese lexical sample WSD dataset with word-formations. Then, we propose a model FormBERT to explicitly incorporate word-formations into sense disambiguation. To further enhance generalizability, we design a word-formation predictor module in case word-formation annotations are unavailable. Experimental results show that our method brings substantial performance improvement over strong baselines.
|
https://aclanthology.org/2021.findings-emnlp.78
|
https://aclanthology.org/2021.findings-emnlp.78.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Exploiting Curriculum Learning in Unsupervised Neural Machine Translation
|
Jinliang Lu, Jiajun Zhang
|
Back-translation (BT) has become one of the de facto components in unsupervised neural machine translation (UNMT), and it explicitly makes UNMT have translation ability. However, all the pseudo bi-texts generated by BT are treated equally as clean data during optimization without considering the quality diversity, leading to slow convergence and limited translation performance. To address this problem, we propose a curriculum learning method to gradually utilize pseudo bi-texts based on their quality from multiple granularities. Specifically, we first apply crosslingual word embedding to calculate the potential translation difficulty (quality) for the monolingual sentences. Then, the sentences are fed into UNMT from easy to hard batch by batch. Furthermore, considering the quality of sentences/tokens in a particular batch are also diverse, we further adopt the model itself to calculate the fine-grained quality scores, which are served as learning factors to balance the contributions of different parts when computing loss and encourage the UNMT model to focus on pseudo data with higher quality. Experimental results on WMT 14 En-Fr, WMT 14 En-De, WMT 16 En-Ro, and LDC En-Zh translation tasks demonstrate that the proposed method achieves consistent improvements with faster convergence speed.
|
https://aclanthology.org/2021.findings-emnlp.79
|
https://aclanthology.org/2021.findings-emnlp.79.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
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Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval
|
Nattapol Trijakwanich, Peerat Limkonchotiwat, Raheem Sarwar, Wannaphong Phatthiyaphaibun, Ekapol Chuangsuwanich, Sarana Nutanong
|
Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. The retrieved parallel sentence pairs can be used in other downstream NLP tasks such as machine translation and cross-lingual word sense disambiguation. We propose a CLSR framework called Robust Fragment-level Representation (RFR) CLSR framework to address Out-of-Domain (OOD) CLSR problems. In particular, we improve the sentence retrieval robustness by representing each sentence as a collection of fragments. In this way, we change the retrieval granularity from the sentence to the fragment level. We performed CLSR experiments based on three OOD datasets, four language pairs, and three base well-known sentence encoders: m-USE, LASER, and LaBSE. Experimental results show that RFR significantly improves the base encoders’ performance for more than 85% of the cases.
|
https://aclanthology.org/2021.findings-emnlp.80
|
https://aclanthology.org/2021.findings-emnlp.80.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Towards Improving Adversarial Training of NLP Models
|
Jin Yong Yoo, Yanjun Qi
|
Adversarial training, a method for learning robust deep neural networks, constructs adversarial examples during training. However, recent methods for generating NLP adversarial examples involve combinatorial search and expensive sentence encoders for constraining the generated instances. As a result, it remains challenging to use vanilla adversarial training to improve NLP models’ performance, and the benefits are mainly uninvestigated. This paper proposes a simple and improved vanilla adversarial training process for NLP models, which we name Attacking to Training (A2T). The core part of A2T is a new and cheaper word substitution attack optimized for vanilla adversarial training. We use A2T to train BERT and RoBERTa models on IMDB, Rotten Tomatoes, Yelp, and SNLI datasets. Our results empirically show that it is possible to train robust NLP models using a much cheaper adversary. We demonstrate that vanilla adversarial training with A2T can improve an NLP model’s robustness to the attack it was originally trained with and also defend the model against other types of word substitution attacks. Furthermore, we show that A2T can improve NLP models’ standard accuracy, cross-domain generalization, and interpretability.
|
https://aclanthology.org/2021.findings-emnlp.81
|
https://aclanthology.org/2021.findings-emnlp.81.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence
|
Caleb Ziems, Diyi Yang
|
Framing has significant but subtle effects on public opinion and policy. We propose an NLP framework to measure entity-centric frames. We use it to understand media coverage on police violence in the United States in a new Police Violence Frames Corpus of 82k news articles spanning 7k police killings. Our work uncovers more than a dozen framing devices and reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. Conservative sources emphasize when the victim is armed or attacking an officer and are more likely to mention the victim’s criminal record. Liberal sources focus more on the underlying systemic injustice, highlighting the victim’s race and that they were unarmed. We discover temporary spikes in these injustice frames near high-profile shooting events, and finally, we show protest volume correlates with and precedes media framing decisions.
|
https://aclanthology.org/2021.findings-emnlp.82
|
https://aclanthology.org/2021.findings-emnlp.82.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Calibrate your listeners! Robust communication-based training for pragmatic speakers
|
Rose Wang, Julia White, Jesse Mu, Noah Goodman
|
To be good conversational partners, natural language processing (NLP) systems should be trained to produce contextually useful utterances. Prior work has investigated training NLP systems with communication-based objectives, where a neural listener stands in as a communication partner. However, these systems commonly suffer from semantic drift where the learned language diverges radically from natural language. We propose a method that uses a population of neural listeners to regularize speaker training. We first show that language drift originates from the poor uncertainty calibration of a neural listener, which makes high-certainty predictions on novel sentences. We explore ensemble- and dropout-based populations of listeners and find that the former results in better uncertainty quantification. We evaluate both population-based objectives on reference games, and show that the ensemble method with better calibration enables the speaker to generate pragmatic utterances while scaling to a large vocabulary and generalizing to new games and listeners.
|
https://aclanthology.org/2021.findings-emnlp.83
|
https://aclanthology.org/2021.findings-emnlp.83.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
|
ZiXian Huang, Ao Wu, Yulin Shen, Gong Cheng, Yuzhong Qu
|
Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.
|
https://aclanthology.org/2021.findings-emnlp.84
|
https://aclanthology.org/2021.findings-emnlp.84.pdf
|
EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Structured abbreviation expansion in context
|
Kyle Gorman, Christo Kirov, Brian Roark, Richard Sproat
|
Ad hoc abbreviations are commonly found in informal communication channels that favor shorter messages. We consider the task of reversing these abbreviations in context to recover normalized, expanded versions of abbreviated messages. The problem is related to, but distinct from, spelling correction, as ad hoc abbreviations are intentional and can involve more substantial differences from the original words. Ad hoc abbreviations are also productively generated on-the-fly, so they cannot be resolved solely by dictionary lookup. We generate a large, open-source data set of ad hoc abbreviations. This data is used to study abbreviation strategies and to develop two strong baselines for abbreviation expansion.
|
https://aclanthology.org/2021.findings-emnlp.85
|
https://aclanthology.org/2021.findings-emnlp.85.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
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Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding
|
Shiyang Li, Semih Yavuz, Wenhu Chen, Xifeng Yan
|
Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data. However, it’s unclear whether they learn similar representations or they can be effectively combined. In this paper, we show that TAPT and ST can be complementary with simple TFS protocol by following TAPT -> Finetuning -> Self-training (TFS) process. Experimental results show that TFS protocol can effectively utilize unlabeled data to achieve strong combined gains consistently across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. We investigate various semi-supervised settings and consistently show that gains from TAPT and ST can be strongly additive by following TFS procedure. We hope that TFS could serve as an important semi-supervised baseline for future NLP studies.
|
https://aclanthology.org/2021.findings-emnlp.86
|
https://aclanthology.org/2021.findings-emnlp.86.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
CNNBiF: CNN-based Bigram Features for Named Entity Recognition
|
Chul Sung, Vaibhava Goel, Etienne Marcheret, Steven Rennie, David Nahamoo
|
Transformer models fine-tuned with a sequence labeling objective have become the dominant choice for named entity recognition tasks. However, a self-attention mechanism with unconstrained length can fail to fully capture local dependencies, particularly when training data is limited. In this paper, we propose a novel joint training objective which better captures the semantics of words corresponding to the same entity. By augmenting the training objective with a group-consistency loss component we enhance our ability to capture local dependencies while still enjoying the advantages of the unconstrained self-attention mechanism. On the CoNLL2003 dataset, our method achieves a test F1 of 93.98 with a single transformer model. More importantly our fine-tuned CoNLL2003 model displays significant gains in generalization to out of domain datasets: on the OntoNotes subset we achieve an F1 of 72.67 which is 0.49 points absolute better than the baseline, and on the WNUT16 set an F1 of 68.22 which is a gain of 0.48 points. Furthermore, on the WNUT17 dataset we achieve an F1 of 55.85, yielding a 2.92 point absolute improvement.
|
https://aclanthology.org/2021.findings-emnlp.87
|
https://aclanthology.org/2021.findings-emnlp.87.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Compositional Generalization via Semantic Tagging
|
Hao Zheng, Mirella Lapata
|
Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i.e., they are unable to systematically generalize to unseen compositions of seen components. Motivated by traditional semantic parsing where compositionality is explicitly accounted for by symbolic grammars, we propose a new decoding framework that preserves the expressivity and generality of sequence-to-sequence models while featuring lexicon-style alignments and disentangled information processing. Specifically, we decompose decoding into two phases where an input utterance is first tagged with semantic symbols representing the meaning of individual words, and then a sequence-to-sequence model is used to predict the final meaning representation conditioning on the utterance and the predicted tag sequence. Experimental results on three semantic parsing datasets show that the proposed approach consistently improves compositional generalization across model architectures, domains, and semantic formalisms.
|
https://aclanthology.org/2021.findings-emnlp.88
|
https://aclanthology.org/2021.findings-emnlp.88.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
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emnlp_findings_2021
|
|||
Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering
|
Zhe Lin, Yitao Cai, Xiaojun Wan
|
Paraphrase generation is an important task in natural language processing. Previous works focus on sentence-level paraphrase generation, while ignoring document-level paraphrase generation, which is a more challenging and valuable task. In this paper, we explore the task of document-level paraphrase generation for the first time and focus on the inter-sentence diversity by considering sentence rewriting and reordering. We propose CoRPG (Coherence Relationship guided Paraphrase Generation), which leverages graph GRU to encode the coherence relationship graph and get the coherence-aware representation for each sentence, which can be used for re-arranging the multiple (possibly modified) input sentences. We create a pseudo document-level paraphrase dataset for training CoRPG. Automatic evaluation results show CoRPG outperforms several strong baseline models on the BERTScore and diversity scores. Human evaluation also shows our model can generate document paraphrase with more diversity and semantic preservation.
|
https://aclanthology.org/2021.findings-emnlp.89
|
https://aclanthology.org/2021.findings-emnlp.89.pdf
|
EMNLP 2021
|
AIM-Harvard/EMNLP-Accepted-Papers
|
default
|
emnlp_findings_2021
|
|||
Exploring Decomposition for Table-based Fact Verification
|
Xiaoyu Yang, Xiaodan Zhu
|
Fact verification based on structured data is challenging as it requires models to understand both natural language and symbolic operations performed over tables. Although pre-trained language models have demonstrated a strong capability in verifying simple statements, they struggle with complex statements that involve multiple operations. In this paper, we improve fact verification by decomposing complex statements into simpler subproblems. Leveraging the programs synthesized by a weakly supervised semantic parser, we propose a program-guided approach to constructing a pseudo dataset for decomposition model training. The subproblems, together with their predicted answers, serve as the intermediate evidence to enhance our fact verification model. Experiments show that our proposed approach achieves the new state-of-the-art performance, an 82.7% accuracy, on the TabFact benchmark.
|
https://aclanthology.org/2021.findings-emnlp.90
|
https://aclanthology.org/2021.findings-emnlp.90.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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emnlp_findings_2021
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Diversity and Consistency: Exploring Visual Question-Answer Pair Generation
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Sen Yang, Qingyu Zhou, Dawei Feng, Yang Liu, Chao Li, Yunbo Cao, Dongsheng Li
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Although showing promising values to downstream applications, generating question and answer together is under-explored. In this paper, we introduce a novel task that targets question-answer pair generation from visual images. It requires not only generating diverse question-answer pairs but also keeping the consistency of them. We study different generation paradigms for this task and propose three models: the pipeline model, the joint model, and the sequential model. We integrate variational inference into these models to achieve diversity and consistency. We also propose region representation scaling and attention alignment to improve the consistency further. We finally devise an evaluator as a quantitative metric for consistency. We validate our approach on two benchmarks, VQA2.0 and Visual-7w, by automatically and manually evaluating diversity and consistency. Experimental results show the effectiveness of our models: they can generate diverse or consistent pairs. Moreover, this task can be used to improve visual question generation and visual question answering.
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https://aclanthology.org/2021.findings-emnlp.91
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https://aclanthology.org/2021.findings-emnlp.91.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Entity-level Cross-modal Learning Improves Multi-modal Machine Translation
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Xin Huang, Jiajun Zhang, Chengqing Zong
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Multi-modal machine translation (MMT) aims at improving translation performance by incorporating visual information. Most of the studies leverage the visual information through integrating the global image features as auxiliary input or decoding by attending to relevant local regions of the image. However, this kind of usage of visual information makes it difficult to figure out how the visual modality helps and why it works. Inspired by the findings of (CITATION) that entities are most informative in the image, we propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation. Specifically, the approach is framed as a reconstruction task that reconstructs the original textural input from multi-modal input in which entities are replaced with visual features. Then, a multi-task framework is employed to combine the translation task and the reconstruction task to make full use of cross-modal entity representation learning. The extensive experiments demonstrate that our approach can achieve comparable or even better performance than state-of-the-art models. Furthermore, our in-depth analysis shows how visual information improves translation.
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https://aclanthology.org/2021.findings-emnlp.92
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https://aclanthology.org/2021.findings-emnlp.92.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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emnlp_findings_2021
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Learning to Ground Visual Objects for Visual Dialog
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Feilong Chen, Xiuyi Chen, Can Xu, Daxin Jiang
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Visual dialog is challenging since it needs to answer a series of coherent questions based on understanding the visual environment. How to ground related visual objects is one of the key problems. Previous studies utilize the question and history to attend to the image and achieve satisfactory performance, while these methods are not sufficient to locate related visual objects without any guidance. The inappropriate grounding of visual objects prohibits the performance of visual dialog models. In this paper, we propose a novel approach to Learn to Ground visual objects for visual dialog, which employs a novel visual objects grounding mechanism where both prior and posterior distributions over visual objects are used to facilitate visual objects grounding. Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process. Meanwhile, a prior distribution, which is inferred from context only, is used to approximate the posterior distribution so that appropriate visual objects can be grounding even without answers during the inference process. Experimental results on the VisDial v0.9 and v1.0 datasets demonstrate that our approach improves the previous strong models in both generative and discriminative settings by a significant margin.
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https://aclanthology.org/2021.findings-emnlp.93
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https://aclanthology.org/2021.findings-emnlp.93.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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emnlp_findings_2021
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KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals
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Jun Zhang, Yan Yang, Chencai Chen, Liang He, Zhou Yu
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Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation. It is beneficial to divide up, such conversations with multiple subgoals (such as social chat, question answering, recommendation, etc.), so that the system can retrieve appropriate knowledge with better accuracy under different subgoals. In this paper, we propose a unified framework for common knowledge-based multi-subgoal dialog: knowledge-enhanced multi-subgoal driven recommender system (KERS). We first predict a sequence of subgoals and use them to guide the dialog model to select knowledge from a sub-set of existing knowledge graph. We then propose three new mechanisms to filter noisy knowledge and to enhance the inclusion of cleaned knowledge in the dialog response generation process. Experiments show that our method obtains state-of-the-art results on DuRecDial dataset in both automatic and human evaluation.
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https://aclanthology.org/2021.findings-emnlp.94
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https://aclanthology.org/2021.findings-emnlp.94.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension
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Haichao Zhu, Zekun Wang, Heng Zhang, Ming Liu, Sendong Zhao, Bing Qin
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In this paper, we propose a simple few-shot domain adaptation paradigm for reading comprehension. We first identify the lottery subnetwork structure within the Transformer-based source domain model via gradual magnitude pruning. Then, we only fine-tune the lottery subnetwork, a small fraction of the whole parameters, on the annotated target domain data for adaptation. To obtain more adaptable subnetworks, we introduce self-attention attribution to weigh parameters, beyond simply pruning the smallest magnitude parameters, which can be seen as combining structured pruning and unstructured magnitude pruning softly. Experimental results show that our method outperforms the full model fine-tuning adaptation on four out of five domains when only a small amount of annotated data available for adaptation. Moreover, introducing self-attention attribution reserves more parameters for important attention heads in the lottery subnetwork and improves the target domain model performance. Our further analyses reveal that, besides exploiting fewer parameters, the choice of subnetworks is critical to the effectiveness.
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https://aclanthology.org/2021.findings-emnlp.95
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https://aclanthology.org/2021.findings-emnlp.95.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Effectiveness of Pre-training for Few-shot Intent Classification
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Haode Zhang, Yuwei Zhang, Li-Ming Zhan, Jiaxin Chen, Guangyuan Shi, Albert Y.S. Lam, Xiao-Ming Wu
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{'url': 'https://github.com/hdzhang-code/IntentBERT', '#text': 'This paper investigates the effectiveness of pre-training for few-shot intent classification. While existing paradigms commonly further pre-train language models such as BERT on a vast amount of unlabeled corpus, we find it highly effective and efficient to simply fine-tune BERT with a small set of labeled utterances from public datasets. Specifically, fine-tuning BERT with roughly 1,000 labeled data yields a pre-trained model – IntentBERT, which can easily surpass the performance of existing pre-trained models for few-shot intent classification on novel domains with very different semantics. The high effectiveness of IntentBERT confirms the feasibility and practicality of few-shot intent detection, and its high generalization ability across different domains suggests that intent classification tasks may share a similar underlying structure, which can be efficiently learned from a small set of labeled data. The source code can be found at .'}
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https://aclanthology.org/2021.findings-emnlp.96
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https://aclanthology.org/2021.findings-emnlp.96.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Improving Abstractive Dialogue Summarization with Hierarchical Pretraining and Topic Segment
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MengNan Qi, Hao Liu, YuZhuo Fu, Ting Liu
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With the increasing abundance of meeting transcripts, meeting summary has attracted more and more attention from researchers. The unsupervised pre-training method based on transformer structure combined with fine-tuning of downstream tasks has achieved great success in the field of text summarization. However, the semantic structure and style of meeting transcripts are quite different from that of articles. In this work, we propose a hierarchical transformer encoder-decoder network with multi-task pre-training. Specifically, we mask key sentences at the word-level encoder and generate them at the decoder. Besides, we randomly mask some of the role alignments in the input text and force the model to recover the original role tags to complete the alignments. In addition, we introduce a topic segmentation mechanism to further improve the quality of the generated summaries. The experimental results show that our model is superior to the previous methods in meeting summary datasets AMI and ICSI.
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https://aclanthology.org/2021.findings-emnlp.97
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https://aclanthology.org/2021.findings-emnlp.97.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Learning to Answer Psychological Questionnaire for Personality Detection
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Feifan Yang, Tao Yang, Xiaojun Quan, Qinliang Su
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Existing text-based personality detection research mostly relies on data-driven approaches to implicitly capture personality cues in online posts, lacking the guidance of psychological knowledge. Psychological questionnaire, which contains a series of dedicated questions highly related to personality traits, plays a critical role in self-report personality assessment. We argue that the posts created by a user contain critical contents that could help answer the questions in a questionnaire, resulting in an assessment of his personality by linking the texts and the questionnaire. To this end, we propose a new model named Psychological Questionnaire enhanced Network (PQ-Net) to guide personality detection by tracking critical information in texts with a questionnaire. Specifically, PQ-Net contains two streams: a context stream to encode each piece of text into a contextual text representation, and a questionnaire stream to capture relevant information in the contextual text representation to generate potential answer representations for a questionnaire. The potential answer representations are used to enhance the contextual text representation and to benefit personality prediction. Experimental results on two datasets demonstrate the superiority of PQ-Net in capturing useful cues from the posts for personality detection.
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https://aclanthology.org/2021.findings-emnlp.98
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https://aclanthology.org/2021.findings-emnlp.98.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Exploiting Reasoning Chains for Multi-hop Science Question Answering
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Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam
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{'i': 'Chain-aware loss', '#text': 'We propose a novel Chain Guided Retriever-reader (CGR) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human-annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. A , concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to maximize the utility of the reasoning chains. Our framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability.'}
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https://aclanthology.org/2021.findings-emnlp.99
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https://aclanthology.org/2021.findings-emnlp.99.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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Winnowing Knowledge for Multi-choice Question Answering
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Yeqiu Li, Bowei Zou, Zhifeng Li, Ai Ti Aw, Yu Hong, Qiaoming Zhu
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We tackle multi-choice question answering. Acquiring related commonsense knowledge to the question and options facilitates the recognition of the correct answer. However, the current reasoning models suffer from the noises in the retrieved knowledge. In this paper, we propose a novel encoding method which is able to conduct interception and soft filtering. This contributes to the harvesting and absorption of representative information with less interference from noises. We experiment on CommonsenseQA. Experimental results illustrate that our method yields substantial and consistent improvements compared to the strong Bert, RoBERTa and Albert-based baselines.
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https://aclanthology.org/2021.findings-emnlp.100
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https://aclanthology.org/2021.findings-emnlp.100.pdf
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EMNLP 2021
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AIM-Harvard/EMNLP-Accepted-Papers
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default
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emnlp_findings_2021
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