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Gender-Inclusive Grammatical Error Correction through Augmentation

Published 12 Jun 2023 in cs.CL | (2306.07415v1)

Abstract: In this paper we show that GEC systems display gender bias related to the use of masculine and feminine terms and the gender-neutral singular "they". We develop parallel datasets of texts with masculine and feminine terms and singular "they" and use them to quantify gender bias in three competitive GEC systems. We contribute a novel data augmentation technique for singular "they" leveraging linguistic insights about its distribution relative to plural "they". We demonstrate that both this data augmentation technique and a refinement of a similar augmentation technique for masculine and feminine terms can generate training data that reduces bias in GEC systems, especially with respect to singular "they" while maintaining the same level of quality.

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References (32)
  1. Lauren Ackerman. 2019. Syntactic and cognitive issues in investigating gendered coreference. Glossa: a journal of general linguistics, 4(1). Number: 1 Publisher: Open Library of Humanities.
  2. Connor Baumler and Rachel Rudinger. 2022. Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3426–3432, Seattle, United States. Association for Computational Linguistics.
  3. Bronwyn M. Bjorkman. 2017. Singular they and the syntactic representation of gender in English. Glossa: a journal of general linguistics, 2(1). Number: 1 Publisher: Open Library of Humanities.
  4. Language (Technology) is Power: A Critical Survey of "Bias" in NLP.
  5. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. Number: arXiv:1607.06520 arXiv:1607.06520 [cs, stat].
  6. The BEA-2019 Shared Task on Grammatical Error Correction. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 52–75, Florence, Italy. Association for Computational Linguistics.
  7. Grammatical Error Correction: A Survey of the State of the Art. ArXiv:2211.05166 [cs].
  8. Yang Trista Cao and Hal Daumé III. 2021. Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*. Computational Linguistics, 47(3):615–661. Place: Cambridge, MA Publisher: MIT Press.
  9. Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies. In EMNLP.
  10. Theories of “Gender” in NLP Bias Research. In 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22, pages 2083–2102, New York, NY, USA. Association for Computing Machinery.
  11. Introducing a gender-neutral pronoun in a natural gender language: the influence of time on attitudes and behavior. Frontiers in Psychology, 6.
  12. Chung-hye Han and Keir Moulton. 2022. Processing bound-variable singular they. Canadian Journal of Linguistics/Revue canadienne de linguistique, 67(3):267–301. Publisher: Cambridge University Press.
  13. Lex Konnelly and Elizabeth Cowper. 2020. Gender diversity and morphosyntax: An account of singular they. Glossa: a journal of general linguistics, 5(1). Number: 1 Publisher: Open Library of Humanities.
  14. The Winograd schema challenge. In Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning, KR’12, pages 552–561, Rome, Italy. AAAI Press.
  15. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871–7880, Online. Association for Computational Linguistics.
  16. RoBERTa: A Robustly Optimized BERT Pretraining Approach. ArXiv:1907.11692 [cs].
  17. Gender Bias in Neural Natural Language Processing. arXiv:1807.11714 [cs]. ArXiv: 1807.11714.
  18. It’s All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution. arXiv:1909.00871 [cs]. ArXiv: 1909.00871.
  19. Sally McConnell-Ginet. 2013. ‘ Gender and its relation to sex: The myth of ‘natural’ gender. In ‘ Gender and its relation to sex: The myth of ‘natural’ gender, pages 3–38. De Gruyter Mouton.
  20. Mining Revision Log of Language Learning SNS for Automated Japanese Error Correction of Second Language Learners. In Proceedings of 5th International Joint Conference on Natural Language Processing, pages 147–155, Chiang Mai, Thailand. Asian Federation of Natural Language Processing.
  21. Singular they in context. Glossa: a journal of general linguistics, 5(1). Number: 1 Publisher: Open Library of Humanities.
  22. GECToR – Grammatical Error Correction: Tag, Not Rewrite. ArXiv:2005.12592 [cs].
  23. Text Simplification by Tagging. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 11–25, Online. Association for Computational Linguistics.
  24. A Simple Recipe for Multilingual Grammatical Error Correction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 702–707, Online. Association for Computational Linguistics.
  25. Gender Bias in Coreference Resolution. arXiv:1804.09301 [cs]. ArXiv: 1804.09301.
  26. Danielle Saunders and Bill Byrne. 2020. Reducing gender bias in neural machine translation as a domain adaptation problem. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7724–7736, Online. Association for Computational Linguistics.
  27. Alexey Sorokin. 2022. Improved grammatical error correction by ranking elementary edits. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11416–11429, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  28. William Strunk and E. B. White. 1999. The elements of style, 4th ed edition. Allyn and Bacon, Boston.
  29. They, Them, Theirs: Rewriting with Gender-Neutral English. ArXiv:2102.06788 [cs].
  30. NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8940–8948, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  31. Measuring and Mitigating Name Biases in Neural Machine Translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2576–2590, Dublin, Ireland. Association for Computational Linguistics.
  32. Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. arXiv:1804.06876 [cs]. ArXiv: 1804.06876.
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