  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 feminine-inflected 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.
