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They, Them, Theirs: Rewriting with Gender-neutral English

Tony Sun
Apu Shah


Research in natural language processing that focuses solely on binary genders can pose the serious danger of excluding communities and behaviors that are gender nonconforming. In this paper, we highlight the use of gender-inclusive language by proposing the task of rewriting gendered sentences in English to be gender-neutral using the \textit{singular they}. To this end, we train a Seq2Seq model for this task by creating a rewriting algorithm to generate a parallel dataset and evaluate performance on an annotated test set of 500 sentence-pairs (gendered to gender-neutral). Impressively, we are able to achieve over 99 BLEU and less than 1\% word error rate for both the algorithm and the model. Finally, we give some practical applications for this task, including machine translation and augmented writing.