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

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8940–8948 November 7–11, 2021. c⃝2021 Association for Computational Linguistics 8940 NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives Eva Vanmassenhove and Chris Emmery and Dimitar Shterionov Department of Cognitive Science and Artificial Intelligence Tilburg University The Netherlands e.o.j.vanmassenhove@tilburguniversity.edu c.d.emmery@tilburguniversity.edu d.shterionov@tilburguniversity.edu 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 lan- guage is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gen- der of referents. In this work, we present a rule- based and a neural approach to gender-neutral rewriting for English along with manually cu- rated 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. 1 Introduction Recent years have seen an increasing need for gender-neutral and inclusive language. This need is reflected, among others, by a surge in the use of singular they, 1 currently endorsed as part of APA style as the generic and gender-neutral pronoun. 2 Within the field of Natural Language Processing (NLP), there are various monolingual and bilingual use cases where gender neutral and inclusive lan- guage is appropriate, if not preferred due to e.g. ambiguity in terms of the gender of referents. Sec- tion 3 provides a short outline of potential NLP use cases. To support these use cases, we present a rule- based and a neural approach to gender-neutral re- writing along with manually curated benchmarks, both of which we provide open-access/source. 3 1. The pronoun ‘they’ was announced word of the year in 2019 according to Merriam Webster https://www.nytimes.com/2019/12/10/us/ merriam-webster-they-word-year.html 2. https://apastyle.apa.org/ 3. https://github.com/ anonymous-until-publication/ NeuTralRewriter First, a rule-based rewriter is implemented leve- raging hand-written rules and an automatic error correction tool. Next, a neural rewriter is trained on output generated by the rule-based rewriter to remove the need for extensive pre-processing and the reliance on computationally expensive tools such as dependency parsers. Our manual and au- tomatic evaluation show how the neural rewriter clearly improves over the rule-based approach with word error rates (WER) below 0.18% on synthetic, in-domain and out-domain test sets. The main contributions of our work can be sum- marized as follows : (i) WinoBias+, an open-source manually curated extension of WinoBias (Zhao et al., 2018a) providing neutral alternatives for 3,167 sentences as well as a manually curated set of 1,000 natural sentences (domain : Reddit, Open- Subtitles), (ii) open-source code for rule-based and neural neutral rewriters which can convert (binary) gendered English sentences into their gender neu- tral counterparts, (iii) a detailed manual and auto- matic evaluation of errors made by the rule-based and neutral rewriter on synthetic and natural data. 2 Related Work Recent years have seen an increase …

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