构建自然语境下的性别模糊翻译数据集，揭示模型如何受上下文影响选择译文性别。
Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution
- 设计含性别模糊的英文句子，模拟真实语言中无明确性别线索场景。
- 通过对比翻译发现，上下文词会影响模型对中性指代的性别判定。
- 适合关注公平性、可解释性的NLP研究者和开发者使用。
机器翻译系统持续产生性别偏见的翻译结果。在自我表达日益重要的当下，基于默认行为和刻板印象的误译可能对用户造成伤害。为更好理解系统在缺乏明确性别线索时如何处理性别翻译，亟需能自然反映性别模糊情境的基准资源。为此，我们提出GAND——一个面向机器翻译的性别模糊自然数据基准，包含精心设计的英文源句，用于分析上下文线索对翻译中性别判断的影响。我们利用GAND开展可解释性分析：将部分GAND语句翻译为两种语法性别语言，并人工构造对比翻译。特征归因分析揭示了影响目标翻译中中性指代实体性别判定的源端词汇。
原文摘要 · Abstract (English)
Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on default behaviour and stereotyping can lead to harm for users of these systems. To better understand how these systems translate gender in the absence of clear gender cues, we need benchmarking resources that reflect gender-ambiguous scenarios in a natural way. To this end, we present GAND, a gender-ambiguous natural data benchmarking resource for MT consisting of English source sentences, specifically designed to analyse the influence of contextual cues on gender in translation. We leverage GAND to conduct an interpretability analysis: we translate a subset of GAND into two grammatical gender languages and extend these with manually crafted contrastive translations. A following feature attribution analysis reveals source words in context that inform the gender translation of an ambiguous referent entity in the target translation.
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