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Leveraging Natural Supervision for Language Representation Learning and Generation: Bibliographyby@textmodels
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Leveraging Natural Supervision for Language Representation Learning and Generation: Bibliography

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In this study, researchers describe three lines of work that seek to improve the training and evaluation of neural models using naturally-occurring supervision.
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Author:

(1) Mingda Chen.

BIBLIOGRAPHY

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Rajas Agashe, Srinivasan Iyer, and Luke Zettlemoyer. 2019. JuICe: A large scale distantly supervised dataset for open domain context-based code generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLPIJCNLP).


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Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor GonzalezAgirre, Weiwei Guo, Inigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, Ger- ˜ man Rigau, Larraitz Uria, and Janyce Wiebe. 2015. SemEval-2015 task 2: Semantic textual similarity, English, Spanish and pilot on interpretability. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015).


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Mingda Chen, Zewei Chu, Yang Chen, Karl Stratos, and Kevin Gimpel. 2019a. EntEval: A holistic evaluation benchmark for entity representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).


Mingda Chen, Zewei Chu, and Kevin Gimpel. 2019b. Evaluation benchmarks and learning criteria for discourse-aware sentence representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).


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