21个机器翻译系统中仅少数能正确处理性别中立翻译，因语言差异策略各异。
Gender-Neutral Machine Translation Strategies in Practice
- 分析21个MT系统在三种翻译方向上的性别中立响应能力
- 多数系统未实现性别中立，仅少数在目标语言支持下采用特定策略
- 揭示二元性别刻板印象对中立翻译的负面影响，适合关注公平AI的研究者
性别包容性机器翻译（MT）应保留源语文本中的性别模糊性，以避免误标性别和代表性伤害。尽管不具语法性别的语言如英语中性别模糊自然存在，但在具有语法性别的语言中保持性别中立却是一个挑战。本文评估了21个MT系统在三种不同难度翻译方向上对性别模糊性需求的敏感度。实际中观察到的性别中立策略被分类讨论，并考察了二元性别刻板印象对性别中立翻译使用的影响。总体而言，我们报告出在应对性别模糊时性别中立翻译的显著缺失。然而，我们发现少数MT系统根据目标语言采用特定策略切换至性别中立翻译。
原文摘要 · Abstract (English)
Gender-inclusive machine translation (MT) should preserve gender ambiguity in the source to avoid misgendering and representational harms. While gender ambiguity often occurs naturally in notional gender languages such as English, maintaining that gender neutrality in grammatical gender languages is a challenge. Here we assess the sensitivity of 21 MT systems to the need for gender neutrality in response to gender ambiguity in three translation directions of varying difficulty. The specific gender-neutral strategies that are observed in practice are categorized and discussed. Additionally, we examine the effect of binary gender stereotypes on the use of gender-neutral translation. In general, we report a disappointing absence of gender-neutral translations in response to gender ambiguity. However, we observe a small handful of MT systems that switch to gender neutral translation using specific strategies, depending on the target language.
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