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

EMNLP (short) 2018


Word embeddings have become a fundamental component in a wide range of Natu-ral Language Processing (NLP) applications.However, these word embeddings trained onhuman-generated corpora inherit strong gen-der stereotypes that reflect social constructs.In this paper, we propose a novel word em-bedding model, De-GloVe, that preserves gen-der 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 informa-tion without sacrificing the functionality of theembedding model. ### Bib entry > @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},
> }