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NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives

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arxiv 2109.06105 v1 pith:4C5IH4O5 submitted 2021-09-13 cs.CL cs.AI

Eva Vanmassenhove , Chris Emmery , Dimitar Shterionov This is my paper
classification cs.CLcs.AI
keywords approachdatagender-neutralrule-basedautomaticgenderinclusivelanguage
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Recent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono- and bilingual use cases where gender inclusive language is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gender of referents. In this work, we present a rule-based and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit) benchmarks. A detailed manual and automatic evaluation highlights how our NeuTral Rewriter, trained on data generated by the rule-based approach, obtains word error rates (WER) below 0.18% on synthetic, in-domain and out-domain test sets.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GeNRe: A French Gender-Neutral Rewriting System Using Collective Nouns

    cs.CL 2025-05 conditional novelty 6.0 of 10

    GeNRe is the first French gender-neutral rewriting system to replace masculine plural member nouns with collective nouns, reaching 3.81% WER with its rule-based version.

  2. Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution

    cs.CL 2025-08 unverdicted novelty 5.0 of 10

    The abstract claims LLMs show up to 40% coreference confidence disparities across intersectional identities, but the article body is an unrelated paper on robotic fruit handling.

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