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.
Cite
@article{arxiv.2109.13767,
title = {Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings},
author = {Vaibhav Kumar and Tenzin Singhay Bhotia and Vaibhav Kumar and Tanmoy Chakraborty},
journal= {arXiv preprint arXiv:2109.13767},
year = {2021}
}