Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals

• URL: http://arxiv.org/abs/2206.01691v1
• Date: Fri, 3 Jun 2022 17:11:00 GMT
• Title: Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals
• Authors: Shiva Omrani Sabbaghi, Aylin Caliskan
• Abstract summary: We demonstrate that word embeddings learn the association between a noun and its grammatical gender in grammatically gendered languages. We show that disentangling grammatical gender signals from word embeddings may lead to improvement in semantic machine learning tasks.
• Score: 3.0349733976070015
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Does the grammatical gender of a language interfere when measuring the semantic gender information captured by its word embeddings? A number of anomalous gender bias measurements in the embeddings of gendered languages suggest this possibility. We demonstrate that word embeddings learn the association between a noun and its grammatical gender in grammatically gendered languages, which can skew social gender bias measurements. Consequently, word embedding post-processing methods are introduced to quantify, disentangle, and evaluate grammatical gender signals. The evaluation is performed on five gendered languages from the Germanic, Romance, and Slavic branches of the Indo-European language family. Our method reduces the strength of grammatical gender signals, which is measured in terms of effect size (Cohen's d), by a significant average of d = 1.3 for French, German, and Italian, and d = 0.56 for Polish and Spanish. Once grammatical gender is disentangled, the association between over 90% of 10,000 inanimate nouns and their assigned grammatical gender weakens, and cross-lingual bias results from the Word Embedding Association Test (WEAT) become more congruent with country-level implicit bias measurements. The results further suggest that disentangling grammatical gender signals from word embeddings may lead to improvement in semantic machine learning tasks.

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