Measuring and Mitigating Name Biases in Neural Machine Translation
Measuring and Mitigating Name Biases in Neural Machine Translation
复制标题
测量和减轻神经机器翻译中的名称偏差
DOI：
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复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Trevor Cohn
中科院分区：
文献类型：
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作者：
Jun Wang;Benjamin I. P. Rubinstein;Trevor Cohn
神经机器翻译（NMT）系统表现出有问题的偏见，例如在将职业术语翻译成具有语法性别的语言时存在刻板的性别偏见。在本文中，我们描述了在NMT系统中普遍存在的一个新的偏见来源，涉及到包含人名的句子的翻译。为了正确翻译这样的句子，NMT系统需要确定名字的性别。我们发现，领先的系统在这项任务上表现得尤其糟糕，尤其是对于女性的名字。这种偏见比名字性别更严重：我们表明，带有模棱两可情绪的术语的翻译也会受到人名的影响，表示种族的专有名词也是如此。为了减轻这些偏差，我们提出了一种简单而有效的基于翻译过程中随机切换实体的数据增强方法，在不影响翻译质量的情况下有效地消除了这一问题。
Neural Machine Translation (NMT) systems exhibit problematic biases, such as stereotypical gender bias in the translation of occupation terms into languages with grammatical gender. In this paper we describe a new source of bias prevalent in NMT systems, relating to translations of sentences containing person names. To correctly translate such sentences, a NMT system needs to determine the gender of the name. We show that leading systems are particularly poor at this task, especially for female given names. This bias is deeper than given name gender: we show that the translation of terms with ambiguous sentiment can also be affected by person names, and the same holds true for proper nouns denoting race. To mitigate these biases we propose a simple but effective data augmentation method based on randomly switching entities during translation, which effectively eliminates the problem without any effect on translation quality.
DOI：
10.18653/v1/n18-2003
发表时间：
2018-04
期刊：
ArXiv
影响因子：
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作者：
Jieyu Zhao;Tianlu Wang;Mark Yatskar;Vicente Ordonez;Kai-Wei Chang
通讯作者：
Jieyu Zhao;Tianlu Wang;Mark Yatskar;Vicente Ordonez;Kai-Wei Chang