mlsanityData to 7 Oct 2026

They, Them, Theirs: Rewriting with Gender-Neutral English

arXiv 2102.06788 ↗First posted 12 Feb 2021cs.CLcited by 44

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Abstract (arXiv)

Responsible development of technology involves applications being inclusive of the diverse set of users they hope to support. An important part of this is understanding the many ways to refer to a person and being able to fluently change between the different forms as needed. We perform a case study on the singular they, a common way to promote gender inclusion in English. We define a re-writing task, create an evaluation benchmark, and show how a model can be trained to produce gender-neutral English with <1% word error rate with no human-labeled data. We discuss the practical applications and ethical considerations of the task, providing direction for future work into inclusive natural language systems.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 12 Feb 2021.

Builds on

17 of 26 references.

References · 17

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  11. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
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5 more
  1. 2018Gender Bias in Coreference Resolutioncited by 438
  2. 2017Six Challenges for Neural Machine Translationcited by 334
  3. 2019Mitigating Gender Bias in Natural Language Processing: Literature Reviewcited by 287
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 7; 2023: 11; 2024: 11; 2025: 5; 2026: 1.

44 papers.

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  1. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  2. 2021Societal Biases in Language Generation: Progress and Challengescited by 201
  3. 2021Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologiescited by 128
  4. 2021Gender Bias in Machine Translationcited by 116
  5. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  6. 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentationcited by 72
  7. 2024Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypescited by 58
  8. 2022Perturbation Augmentation for Fairer NLPcited by 51
  9. 2021NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternativescited by 46
  10. 2021Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgettingcited by 34
  11. 2021First the worst: Finding better gender translations during beam searchcited by 28
  12. 2022Text Style Transfer for Bias Mitigation using Masked Language Modelingcited by 27
32 more
  1. 2022Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translationcited by 23
  2. 2022Challenges in Measuring Bias via Open-Ended Language Generationcited by 23
  3. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
  4. 2023Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challengescited by 22
  5. 2023Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpuscited by 20
  6. 2023GATE: A Challenge Set for Gender-Ambiguous Translation Examplescited by 18
  7. 2022How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronounscited by 16
  8. 2024From 'Showgirls' to 'Performers': Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMscited by 15
  9. 2023Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Modelcited by 14
  10. 2024Harmful Speech Detection by Language Models Exhibits Gender-Queer Dialect Biascited by 14
  11. 2023Queer People are People First: Deconstructing Sexual Identity Stereotypes in Large Language Modelscited by 13
  12. 2021The Arabic Parallel Gender Corpus 2.0: Extensions and Analysescited by 12
  13. 2024A Prompt Response to the Demand for Automatic Gender-Neutral Translationcited by 12
  14. 2023Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societiescited by 11
  15. 2023Measuring Gender Bias in West Slavic Language Modelscited by 10
  16. 2022User-Centric Gender Rewritingcited by 7
  17. 2025Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translationcited by 7
  18. 2023Gender-Inclusive Grammatical Error Correction through Augmentationcited by 6
  19. 2023Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Modelscited by 6
  20. 2024MisgenderMender: A Community-Informed Approach to Interventions for Misgenderingcited by 6
  21. 2025Agree to Disagree? A Meta-Evaluation of LLM Misgenderingcited by 6
  22. 2024Gender Bias in Machine Translation and The Era of Large Language Modelscited by 4
  23. 2024GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languagescited by 4
  24. 2024The Life Cycle of Large Language Models: A Review of Biases in Educationcited by 3
  25. 2024Generating Gender Alternatives in Machine Translationcited by 3
  26. 2022The User-Aware Arabic Gender Rewritercited by 1
  27. 2025Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine Generics Biascited by 1
  28. 2025GeNRe: A French Gender-Neutral Rewriting System Using Collective Nounscited by 1
  29. 2026Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architecturescited by 1
  30. 2023Evaluating Gender Bias in the Translation of Gender-Neutral Languages into Englishcited by 0
  31. 2024Pronoun Logiccited by 0
  32. 2025Gender-Neutral Rewriting in Italian: Models, Approaches, and Trade-offscited by 0