超越“男性代码”：NLP语境中的隐性男性偏见
Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts
AI总结：
本文考察两个NLP数据集，揭示性别化语言中普遍存在且随语境变化的隐性男性偏见，并发布AVA词典以刻画虚拟助手语言中的性别化模糊关联。
摘要：
批判性学术研究已经提升了用于训练虚拟助手（VA）的数据集当中性别偏见问题的重要性。大多数工作聚焦于语言中的显性偏见，尤其是针对女性、女孩、自我认同为femme的人群以及性别酷儿群体的偏见；通过词嵌入呈现的隐性关联；以及关于性别与男性气质的有限模型，特别是有毒男性气质、生理性别与社会性别混同，以及将男性气质框定为与女性气质截然对立的生理性别/社会性别二元框架。然而，我们还必须审视男性气质如何被“编码”进语言，以及“男性”被假定为语言默认项的问题：这就是隐性男性偏见。为此，我们考察了两个自然语言处理（NLP）数据集。我们发现，当存在性别化语言时，也会存在性别偏见，尤其是男性偏见。此外，这些偏见以细致而微妙的方式与NLP语境相关联。我们提供了一个名为AVA的新词典，涵盖性别化语言与VA语言之间的模糊关联。
英文摘要：
Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are "coded" into language and the assumption of "male" as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
