Learning Gender-Neutral Word Embeddings

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


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.
Anthology ID:
D18-1521
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4847–4853
Language:
URL:
https://aclanthology.org/D18-1521
DOI:
10.18653/v1/D18-1521
Bibkey:
Cite (ACL):
Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. 2018. Learning Gender-Neutral Word Embeddings. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4847–4853, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Learning Gender-Neutral Word Embeddings (Zhao et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1521.pdf
Code
 uclanlp/gn_glove
Data
WinoBias