Counterfactual Data Augmentation for Mitigating Gender Stereotypes in
Languages with Rich Morphology
Abstract
Gender stereotypes are manifest in most of the
world’s languages and are consequently propagated or amplified by NLP systems. Although
research has focused on mitigating gender
stereotypes in English, the approaches that are
commonly employed produce ungrammatical
sentences in morphologically rich languages.
We present a novel approach for converting between masculine-inflected and feminineinflected sentences in such languages. For
Spanish and Hebrew, our approach achieves
F1 scores of 82% and 73% at the level of tags
and accuracies of 90% and 87% at the level of
forms. By evaluating our approach using four
different languages, we show that, on average,
it reduces gender stereotyping by a factor of
2.5 without any sacrifice to grammaticality