# Gender-Inclusive Grammatical Error Correction through Augmentation

arXiv: https://arxiv.org/abs/2306.07415 · HTML: https://mlsanity.com/paper/2306.07415/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [Gender Bias in Neural Natural Language Processing](https://mlsanity.com/paper/1807.11714/) (2018)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021)
- [A Simple Recipe for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2106.03830/) (2021)
- [It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution](https://mlsanity.com/paper/1909.00871/) (2019)
- [Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem](https://mlsanity.com/paper/2004.04498/) (2020)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives](https://mlsanity.com/paper/2109.06105/) (2021)
- [They, Them, Theirs: Rewriting with Gender-Neutral English](https://mlsanity.com/paper/2102.06788/) (2021)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [Text Simplification by Tagging](https://mlsanity.com/paper/2103.05070/) (2021)

## Built on by (most cited first, 6 of 6)

- [Fairness in Large Language Models: A Taxonomic Survey](https://mlsanity.com/paper/2404.01349/) (2024)
- [From 'Showgirls' to 'Performers': Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMs](https://mlsanity.com/paper/2407.04434/) (2024)
- [Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction](https://mlsanity.com/paper/2402.09683/) (2024)
- [MisgenderMender: A Community-Informed Approach to Interventions for Misgendering](https://mlsanity.com/paper/2404.14695/) (2024)
- [Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs](https://mlsanity.com/paper/2409.13852/) (2024)
- [Analyzing values about gendered language reform in LLMs' revisions](https://mlsanity.com/paper/2505.21378/) (2025)

## 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.