Learning Gender-Neutral Word Embeddings

Published in EMNLP, 2018

Recommended citation: Bibtex
http://aclweb.org/anthology/D18-1521

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

Abstract

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.

Citation:

@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}, }