Update README.md

README.md

@@ -51,10 +51,62 @@ size_categories:
 
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+This repository contains the English '[SemEval-2014 Task 4: Aspect Based Sentiment Analysis](https://aclanthology.org/S14-2004/)'. translated with DeepL into Spanish, French, Russian, and Turkish. The **labels have been manually projected**. For more details, read this paper:  [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://arxiv.org/abs/2210.12623). 
+
+**Intended Usage**: Since the datasets are parallel across languages, they are ideal for evaluating annotation projection algorithms, such as [T-Projection](https://arxiv.org/abs/2212.10548). 
+
+
+# Label Dictionary
+
 ```python
 {
 "O": 0,
 "B-TARGET": 1,
 "I-TARGET": 2
 }
+```
+
+# Cication
+
+If you use this data, please cite the following papers:
+
+```bibtex
+@inproceedings{garcia-ferrero-etal-2022-model,
+    title = "Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings",
+    author = "Garc{\'\i}a-Ferrero, Iker  and
+      Agerri, Rodrigo  and
+      Rigau, German",
+    editor = "Goldberg, Yoav  and
+      Kozareva, Zornitsa  and
+      Zhang, Yue",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.findings-emnlp.478",
+    doi = "10.18653/v1/2022.findings-emnlp.478",
+    pages = "6403--6416",
+    abstract = "Zero-resource cross-lingual transfer approaches aim to apply supervised modelsfrom a source language to unlabelled target languages. In this paper we performan in-depth study of the two main techniques employed so far for cross-lingualzero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translation and annotation projection(data-based cross-lingual transfer) as an effective technique for cross-lingualsequence labelling, in this paper we experimentally demonstrate that highcapacity multilingual language models applied in a zero-shot (model-basedcross-lingual transfer) setting consistently outperform data-basedcross-lingual transfer approaches. A detailed analysis of our results suggeststhat this might be due to important differences in language use. Morespecifically, machine translation often generates a textual signal which isdifferent to what the models are exposed to when using gold standard data,which affects both the fine-tuning and evaluation processes. Our results alsoindicate that data-based cross-lingual transfer approaches remain a competitiveoption when high-capacity multilingual language models are not available.",
+}
+
+@inproceedings{pontiki-etal-2014-semeval,
+    title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
+    author = "Pontiki, Maria  and
+      Galanis, Dimitris  and
+      Pavlopoulos, John  and
+      Papageorgiou, Harris  and
+      Androutsopoulos, Ion  and
+      Manandhar, Suresh",
+    editor = "Nakov, Preslav  and
+      Zesch, Torsten",
+    booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)",
+    month = aug,
+    year = "2014",
+    address = "Dublin, Ireland",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/S14-2004",
+    doi = "10.3115/v1/S14-2004",
+    pages = "27--35",
+}
 ```