Papers › An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in...
An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models
Victor Steinborn, Philipp Dufter, Haris Jabbar, Hinrich Schuetze
Bias research in NLP is a rapidly growing and developing field. Similar to CrowS-Pairs (Nangia et al., 2020), we assess gender bias in masked-language models (MLMs) by studying pairs of sentences with gender swapped person references.Most bias research focuses on and often is specific to English.Using a novel methodology for creating sentence pairs that is applicable across languages, we create, based on CrowS-Pairs, a multilingual dataset for English, Finnish, German, Indonesian and Thai.Additionally, we propose S_{JSD}, a new bias measure based on Jensen–Shannon divergence, which we argue retains more information from the model output probabilities than other previously proposed bias measures for MLMs.Using multilingual MLMs, we find that S_{JSD} diagnoses the same systematic biased behavior for non-English that previous studies have found for monolingual English pre-trained MLMs. S_{JSD} outperforms the CrowS-Pairs measure, which struggles to find such biases for smaller non-English datasets.
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