mlsanityData to 7 Oct 2026

Gender-Inclusive Grammatical Error Correction through Augmentation

arXiv 2306.07415 ↗First posted 12 Jun 2023cs.CLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 12 Jun 2023.

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17 of 37 references.

References · 17

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  4. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  5. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
  6. 2018Gender Bias in Coreference Resolutioncited by 438
  7. 2018Gender Bias in Neural Natural Language Processingcited by 210
  8. 2020GECToR -- Grammatical Error Correction: Tag, Not Rewritecited by 177
  9. 2021Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologiescited by 128
  10. 2021A Simple Recipe for Multilingual Grammatical Error Correctioncited by 108
  11. 2019It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitutioncited by 102
  12. 2020Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problemcited by 86
5 more
  1. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  2. 2021NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternativescited by 46
  3. 2021They, Them, Theirs: Rewriting with Gender-Neutral Englishcited by 44
  4. 2022Grammatical Error Correction: A Survey of the State of the Artcited by 43
  5. 2021Text Simplification by Taggingcited by 27

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Cited by year

Citing papers per year: 2024: 5; 2025: 1.

6 papers.

  1. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 57
  2. 2024From 'Showgirls' to 'Performers': Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMscited by 15
  3. 2024Exploring a Behavioral Model of "Positive Friction" in Human-AI Interactioncited by 9
  4. 2024MisgenderMender: A Community-Informed Approach to Interventions for Misgenderingcited by 6
  5. 2024Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMscited by 5
  6. 2025Analyzing values about gendered language reform in LLMs' revisionscited by 1