Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation between grammatical gender languages which requires preserving the inherent gender information from the source in the target language. We study the effect of encouraging language-agnostic hidden representations on models' ability to preserve gender and compare pivot-based and zero-shot translation regarding the influence of the bridge language (participating in all language pairs during training) on gender preservation. We find that language-agnostic representations mitigate zero-shot models' masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement.

翻译：神经机器翻译（NMT）模型常常带有性别偏见，对用户和整个社会造成损害。在本研究中，我们探讨了弥合缺乏平行数据的语言之间的鸿沟如何影响多语言NMT中的性别偏见，特别是零样本方向。我们评估了需要将源语言固有的性别信息保留到目标语言的语法性别语言之间的翻译。我们研究了鼓励语言无关的隐藏表示对模型保留性别能力的影响，并比较了基于枢轴和零样本翻译中桥梁语言（在训练过程中参与所有语言对）对性别保留的影响。我们发现，语言无关的表示减轻了零样本模型的男性偏见，并且随着桥梁语言中性别屈折变化程度的增加，枢轴翻译在说话者相关性别一致性的公平性别保留方面超过了零样本翻译。