Evoluce genderových forem a zaujatosti ve vícejazyčných korpusech

Jurášová, Daniela

Although state-of-the-art machine translation models achieve high translation quality, they often exhibit bias. The imbalance of gender forms in the training data was identified as the key source of gender bias. The aim of this work is to study the evolution of gender forms in the data and subsequently mitigate the gender bias of machine translation models. We focus on languages with morphological gender (Czech, German, Spanish and Polish). We thoroughly analyze the development of the frequency of gendered occupations in the data over time and report a steady but slow trend in the increased frequency of female occupations. We then curate the available natural data based on temporal and topic analysis to obtain a gender-balanced portion, and perform experiments with fine- tuning on such data. We report a reduction in the gender bias of the models and increased accuracy of translating to the correct gender with a slight decrease in translation quality. This confirms the benefit of debiasing techniques based on fine-tuning models on balanced data. We contribute a novel method for obtaining gender-balanced data from available natural data and emphasize the significant presence of stereotypes in the data and the need to minimize them.

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