论文标题
如何在机器翻译中测量性别偏见：最佳翻译器，多个参考点
How to Measure Gender Bias in Machine Translation: Optimal Translators, Multiple Reference Points
论文作者
论文摘要
在本文中，作为一个案例研究，我们介绍了与Google翻译机器翻译中性别偏差的系统研究。我们将包含来自匈牙利的职业名称（一种具有性别中立代词的语言）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器进行比较来提出公平的偏见衡量标准。评估偏见时，我们使用以下参考点：（1）源头和目标语言国家的职业中男女分布，以及（2）匈牙利调查的结果，该调查检查是否通常被认为是女性或男性化的。我们还研究了如何用形容词的扩展句子涉及职业效果翻译代词的性别。结果，我们发现了对这两个性别的偏见，但是对女性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，与形容词相比，职业对翻译的影响更大。
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.
