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

Eva Vanmassenhove, Chris Emmery, Dimitar Shterionov
2021 Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing   unpublished
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 rulebased and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit)
more » ... 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.
doi:10.18653/v1/2021.emnlp-main.704 fatcat:fgz6raqp6ffh3mjajjw5qragoq