Shenggang Hu

Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic\n Information Preserving

2021 · arXiv (Cornell University)

With widening deployments of natural language processing (NLP) in daily life,\ninherited social biases from NLP models have become more severe and\nproblematic. Previous studies have shown that word embeddings trained on\nhuman-generated corpora have strong gender biases …