Gender-GAP 流水线：面向55种语言性别特征刻画的多语言性别感知流水线
The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages
AI总结：
本文提出Gender-GAP流水线，利用多语言性别化人称名词词典自动量化55种语言文本中的性别表征，并应用于WMT数据，揭示其男性偏向，建议通过数据增强实现平衡。
摘要：
语言生成系统中的性别偏见难以缓解。这些偏见的一个可能来源是训练和评估数据中的性别表征差异。尽管近期在记录这一问题和尝试缓解偏见方面取得了进展，我们仍然缺乏共享的方法论和工具来报告大型数据集中的性别表征。这种定量报告将有助于进一步的缓解措施，例如通过数据增强。本文描述了Gender-GAP流水线（即性别感知多语言流水线），一种自动化的流水线，用于刻画55种语言的大规模数据集中的性别表征。该流水线使用多语言性别化人称名词词典来量化文本中的性别表征。我们展示了该流水线在WMT训练数据和新闻任务开发数据中的性别表征报告，确认当前数据偏向于男性表征。不平衡的数据集可能间接导致我们的系统在优化时偏向于某一性别而非其他性别。我们建议在当前数据集中引入我们的性别量化流水线，并理想情况下修改这些数据集以实现平衡的表征。
英文摘要：
Gender biases in language generation systems are challenging to mitigate. One possible source for these biases is gender representation disparities in the training and evaluation data. Despite recent progress in documenting this problem and many attempts at mitigating it, we still lack shared methodology and tooling to report gender representation in large datasets. Such quantitative reporting will enable further mitigation, e.g., via data augmentation. This paper describes the Gender-GAP Pipeline (for Gender-Aware Polyglot Pipeline), an automatic pipeline to characterize gender representation in large-scale datasets for 55 languages. The pipeline uses a multilingual lexicon of gendered person-nouns to quantify the gender representation in text. We showcase it to report gender representation in WMT training data and development data for the News task, confirming that current data is skewed towards masculine representation. Having unbalanced datasets may indirectly optimize our systems towards outperforming one gender over the others. We suggest introducing our gender quantification pipeline in current datasets and, ideally, modifying them toward a balanced representation.
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