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.

神经机器翻译模型在其训练数据分布中往往会延续性别偏见。本文通过详细分析英语到德语的刻板职业翻译以及巴斯克语到西班牙语的非信息上下文翻译，检验了上下文感知模型对减轻此类偏见的作用。研究结果表明，虽然上下文感知模型可以显著增强女性术语的翻译准确性，但依然可能保持或放大性别偏见。这些结果凸显了神经机器翻译中对偏见减轻需要更精细方法的需求。