Does Context Help Mitigate Gender Bias in Neural Machine Translation?
Harritxu Gete, Thierry Etchegoyhen · 2024
Neural Machine Translation models tend to perpetuate gender bias present in their training data distribution.Context-aware models have been previously suggested as a means to mitigate this type of bias.In this work, we examine this claim by analysing in detail the translation of stereotypical professions in English to German, and translation with non-informative context in Basque to Spanish.Our results show that, although context-aware models can significantly enhance translation accuracy for feminine terms, they can still maintain or even amplify gender bias.These results highlight the need for more fine-grained approaches to bias mitigation in Neural Machine Translation.