In this paper, as a case study, we present a systematic study of gender bias in machine translation with Google Translate. We translated sentences containing names of occupations from Hungarian, a language with gender-neutral pronouns, into English. Our aim was to present a fair measure for bias by comparing the translations to an optimal non-biased translator. When assessing bias, we used the following reference points: (1) the distribution of men and women among occupations in both the source and the target language countries, as well as (2) the results of a Hungarian survey that examined if certain jobs are generally perceived as feminine or masculine. We also studied how expanding sentences with adjectives referring to occupations effect the gender of the translated pronouns. As a result, we found bias against both genders, but biased results against women are much more frequent. Translations are closer to our perception of occupations than to objective occupational statistics. Finally, occupations have a greater effect on translation than adjectives.

翻译：在本文中,作为案例研究,我们以Google Translate为例,对机器翻译中的性别偏见进行系统研究。我们把含有匈牙利语(一种不分性别的代名词语言)等职业名称的句子翻译成英文。我们的目的是通过将翻译与最佳的无偏见翻译进行比较,提出一种公平的偏见衡量标准。我们在评估偏见时,使用了以下参考点:(1) 来源国和目标语言国家中男女职业的分布,以及(2) 匈牙利调查的结果,调查某些工作是否普遍被视为女性或男性工作。我们还研究了扩大句子时如何用形容词来形容职业对被翻译的代名词的性别产生影响。结果结果是,我们发现两种性别都存在偏见,但对妇女的偏见结果更为频繁。翻译更接近我们对职业的看法,而不是客观的职业统计。最后,职业对翻译的影响大于形容词。