@inproceedings{vanmassenhove-etal-2021-neutral,
    title = "{N}eu{T}ral {R}ewriter: {A} Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives",
    author = "Vanmassenhove, Eva  and
      Emmery, Chris  and
      Shterionov, Dimitar",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.emnlp-main.704/",
    doi = "10.18653/v1/2021.emnlp-main.704",
    pages = "8940--8948",
    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."
}