Learning Gender-Neutral Word Embeddings

Understand

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.