NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives
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Abstract (arXiv)
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.
Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 13 Sep 2021.
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References · 19
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- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
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- 2019Getting Gender Right in Neural Machine Translationcited by 98
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Citing papers per year: 2021: 1; 2022: 4; 2023: 11; 2024: 14; 2025: 14; 2026: 2.
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- 2024Generating Gender Alternatives in Machine Translation29 Jul 2024
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- 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
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