Contextualized word embeddings have been replacing standard embeddings as the representational knowledge source of choice in NLP systems. Since a variety of biases have previously been found in standard word embeddings, it is crucial to assess biases encoded in their replacements as well. Focusing on BERT (Devlin et al., 2018), we measure gender bias by studying associations between gender-denoting target words and names of professions in English and German, comparing the findings with real-world workforce statistics. We mitigate bias by fine-tuning BERT on the GAP corpus (Webster et al., 2018), after applying Counterfactual Data Substitution (CDS) (Maudslay et al., 2019). We show that our method of measuring bias is appropriate for languages such as English, but not for languages with a rich morphology and gender-marking, such as German. Our results highlight the importance of investigating bias and mitigation techniques cross-linguistically, especially in view of the current emphasis on large-scale, multilingual language models.

研究比较了英语和德语中职业名称和性别指示目标词之间的关联性，并使用Bert模型来检测性别偏见，结果表明非常适合英语，但不适合具有丰富的形态和性别标记的德语等语言，本文强调探究偏见和减轻技术的重要性，特别是在大规模，多语言的语言模型中。

揭示背景刻板印象：测量和减轻BERT 的性别偏见