Zhao et al. (2018): Learning gender-neutral word embeddings
Abstract Word embedding models have become a fundamental component in a wide range of Natural Language Processing (NLP) applications. However, embeddings trained on human-generated corpora have been demonstrated to inherit strong gender stereotypes that reflect social constructs. To address this concern, in this paper, we propose a novel training procedure for learning gender-neutral word embeddings. Our approach aims to preserve gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence. Based on the proposed method, we generate a Gender-Neutral variant of GloVe (GN-GloVe). Quantitative and qualitative experiments demonstrate that GN-GloVe successfully isolates gender information without sacrificing the functionality of the embedding model.Click here to access the article.
Citation Zhao, J.; Zhou, Y.; Li, Z.; Wang, W.; Chang, K. (2018): Learning gender-neutral word embeddings. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics: 4847– 4853.