Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings

• URL: http://arxiv.org/abs/2109.13767v1
• Date: Tue, 28 Sep 2021 14:43:37 GMT
• Title: Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings
• Authors: Vaibhav Kumar, Tenzin Singhay Bhotia, Vaibhav Kumar and Tanmoy Chakraborty
• Abstract summary: We extend the study of gender bias to the recently popularized hyperbolic word embeddings. We propose gyrocosine bias, a novel measure for quantifying gender bias in hyperbolic word representations. Experiments on a suit of evaluation tests show that Poincar'e Gender Debias (PGD) effectively reduces bias while adding a minimal semantic offset.
• Score: 34.378806636170616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Euclidean word embedding models such as GloVe and Word2Vec have been shown to reflect human-like gender biases. In this paper, we extend the study of gender bias to the recently popularized hyperbolic word embeddings. We propose gyrocosine bias, a novel measure for quantifying gender bias in hyperbolic word representations and observe a significant presence of gender bias. To address this problem, we propose Poincar\'e Gender Debias (PGD), a novel debiasing procedure for hyperbolic word representations. Experiments on a suit of evaluation tests show that PGD effectively reduces bias while adding a minimal semantic offset.

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