Evaluating Gender Bias in Machine Translation
Evaluating Gender Bias in Machine Translation
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DOI：
10.18653/v1/p19-1164
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发表时间：
2019-06
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影响因子：
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通讯作者：
Gabriel Stanovsky;Noah A. Smith;Luke Zettlemoyer
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作者：
Gabriel Stanovsky;Noah A. Smith;Luke Zettlemoyer
我们提出了第一个挑战集和评估协议，用于分析机器翻译（MT）中的性别偏见。我们的方法使用了两个最近的共指消解数据集，这些数据集由英语句子组成，这些句子将参与者塑造成非刻板的性别角色（例如，“医生请护士在手术中帮助她”）。我们设计了一种基于词法分析的八种具有语法性别的目标语言的自动性别偏见评估方法（例如，“医生”一词的女性变音）。我们的分析表明，四个流行的工业机器翻译系统和两个最近的国家的最先进的学术机器翻译模型是显着倾向于性别偏见的翻译错误的所有测试的目标语言。我们的数据和代码可在https://github.com/gabrielStanovsky/mt_gender上公开获取。
We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., “The doctor asked the nurse to help her in the operation”). We devise an automatic gender bias evaluation method for eight target languages with grammatical gender, based on morphological analysis (e.g., the use of female inflection for the word “doctor”). Our analyses show that four popular industrial MT systems and two recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all tested target languages. Our data and code are publicly available at https://github.com/gabrielStanovsky/mt_gender.