Gender-Inclusive Grammatical Error Correction through Augmentation
Gunnar Lund Kostiantyn Omelianchuk Igor Samokhin
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.