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.

研究发现，语言的语法性别可能会影响词嵌入的性别信息，提出了一种后处理方法来度量、分离和评价语法性别信号，检验发现此方法有效地减少了法语、德语、意大利语的效应量，并认为分离语法性别信号可以提高语义机器学习任务的性能。