检测英译希中性别偏见，发现主流翻译系统在无性别提示时易强化刻板印象。
Gender Bias in English-to-Greek Machine Translation
- 构建240句双语数据集GendEL，覆盖职业名词与形容词的性别模糊/明确场景。
- 谷歌翻译与DeepL在性别明确时表现良好，但无性别提示时仍存显著偏见。
- GPT-4o可生成性别明确或中性替代方案，是潜在的偏见缓解工具。
随着对包容性语言需求的增长，机器翻译（MT）系统可能强化性别刻板印象的问题日益受到关注。本研究聚焦于较少被研究的英译希语言对，考察两个商用翻译系统——Google Translate与DeepL中的性别偏见。分析涵盖三方面：男性偏见、职业刻板印象以及反刻板印象翻译错误。此外，探索了提示版GPT-4o作为偏见缓解工具的潜力，可在必要时提供性别明确或中性的替代译文。为此，我们构建了名为GendEL的手动标注双语数据集，包含240个性别模糊与明确的句子，涉及典型的职业名词与形容词。结果显示，两种MT系统在性别明确的情况下表现良好，其中DeepL在女性性别明确句上优于谷歌翻译和GPT-4o；然而，在性别未指定情况下，二者均未能生成性别包容或中性的翻译。GPT-4o展现出潜力，多数模糊案例中能生成适当性别化或中性替代译文，但残余偏见依然存在。
原文摘要 · Abstract (English)
As the demand for inclusive language increases, concern has grown over the susceptibility of machine translation (MT) systems to reinforce gender stereotypes. This study investigates gender bias in two commercial MT systems, Google Translate and DeepL, focusing on the understudied English-to-Greek language pair. We address three aspects of gender bias: i) male bias, ii) occupational stereotyping, and iii) errors in anti-stereotypical translations. Additionally, we explore the potential of prompted GPT-4o as a bias mitigation tool that provides both gender-explicit and gender-neutral alternatives when necessary. To achieve this, we introduce GendEL, a manually crafted bilingual dataset of 240 gender-ambiguous and unambiguous sentences that feature stereotypical occupational nouns and adjectives. We find persistent gender bias in translations by both MT systems; while they perform well in cases where gender is explicitly defined, with DeepL outperforming both Google Translate and GPT-4o in feminine gender-unambiguous sentences, they are far from producing gender-inclusive or neutral translations when the gender is unspecified. GPT-4o shows promise, generating appropriate gendered and neutral alternatives for most ambiguous cases, though residual biases remain evident.
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