Gender-Inclusive Grammatical Error Correction through Augmentation

Abstract

In this paper we show that GEC systems display gender bias related to the use of masculine and feminine terms and the gender-neutral singular they. We develop parallel datasets of texts with masculine and feminine terms, and singular they, and use them to quantify gender bias in three competitive GEC systems. We contribute a novel data augmentation technique for singular they leveraging linguistic insights about its distribution relative to plural they. We demonstrate that both this data augmentation technique and a refinement of a similar augmentation technique for masculine and feminine terms can generate training data that reduces bias in GEC systems, especially with respect to singular they while maintaining the same level of quality.