A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations

Word embedding has become essential for natural language processing as it boosts empirical performances of various tasks. However, recent research discovers that gender bias is incorporated in neural word embeddings, and downstream tasks that rely on these biased word vectors also produce gender-biased results. While some word-embedding genderdebiasing methods have been developed, these methods mainly focus on reducing gender bias associated with gender direction and fail to reduce the gender bias presented in word embedding relations. In this paper, we design a causal and simple approach for mitigating gender bias in word vector relation by utilizing the statistical dependency between genderdefinition word embeddings and gender-biased word embeddings. Our method attains state-of-the-art results on genderdebiasing tasks, lexical- and sentence-level evaluation tasks, and downstream coreference resolution tasks. .

Authors

• Zekun Yang
• Juan Feng

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