论文标题
对跨多种语言中BERT变体中存在的社会偏见的分析
An Analysis of Social Biases Present in BERT Variants Across Multiple Languages
论文作者
论文摘要
尽管大型的预训练的语言模型在许多NLP任务中取得了巨大的成功，但已表明它们反映了人类培训前的偏见。当这些模型应用于现实世界设置时，这种偏见可能会导致不良结果。在本文中，我们研究了各种语言（英语，希腊语和波斯语）的单语BERT模型中存在的偏见。尽管最近的研究主要集中在与性别相关的偏见上，但我们也分析了宗教和种族偏见，并提出了一种基于模板的方法来测量任何基于句子伪类似类型的偏见，可以处理具有基于性别的形容词形容词的形态上的形态复杂语言。我们通过这种方法分析了每个单语言模型，并可视化偏见不同维度之间的文化相似性和差异。最终，我们得出的结论是，当前的偏见探测方法是高度依赖语言的，需要对每种语言和文化中偏见的独特方式（例如，通过编码语言，Synecdoche和其他类似的语言概念）的独特方式进行文化见解。我们还假设，在非英语BERT模型中，较高的测量社会偏见与培训中用户生成的内容相关。
Although large pre-trained language models have achieved great success in many NLP tasks, it has been shown that they reflect human biases from their pre-training corpora. This bias may lead to undesirable outcomes when these models are applied in real-world settings. In this paper, we investigate the bias present in monolingual BERT models across a diverse set of languages (English, Greek, and Persian). While recent research has mostly focused on gender-related biases, we analyze religious and ethnic biases as well and propose a template-based method to measure any kind of bias, based on sentence pseudo-likelihood, that can handle morphologically complex languages with gender-based adjective declensions. We analyze each monolingual model via this method and visualize cultural similarities and differences across different dimensions of bias. Ultimately, we conclude that current methods of probing for bias are highly language-dependent, necessitating cultural insights regarding the unique ways bias is expressed in each language and culture (e.g. through coded language, synecdoche, and other similar linguistic concepts). We also hypothesize that higher measured social biases in the non-English BERT models correlate with user-generated content in their training.
