Learning Gender-Neutral Word Embeddings

Published in EMNLP, 2018

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Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, Kai-Wei Chang

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Word embeddings have become a fundamental component in a wide range of Natu-ral Language Processing (NLP) applications.However, these word embeddings trained on human-generated corpora inherit strong gender stereotypes that reflect social constructs. In this paper, we propose a novel word embedding model, De-GloVe, that preserves gender information in certain dimensions of wordvectors while compelling other dimensions tobe free of gender influence. Quantitative andqualitative experiments demonstrate that De-GloVe successfully isolates gender information without sacrificing the functionality of the embedding model.


@inproceedings{ZZLWC18, author = {Jieyu Zhao and Yichao Zhou and Zeyu Li and Wei Wang and Kai-Wei Chang}, title = {Learning Gender-Neutral Word Embeddings}, booktitle = {EMNLP (short)}, year = {2018}, }