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

• URL: http://arxiv.org/abs/2109.06105v1
• Date: Mon, 13 Sep 2021 16:26:12 GMT
• Title: NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives
• Authors: Eva Vanmassenhove, Chris Emmery and Dimitar Shterionov
• Abstract summary: We present a rule-based and a neural approach to gender-neutral rewriting for English. 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%.
• Score: 2.3204178451683264
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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