On Measuring and Mitigating Biased Inferences of Word Embeddings

Sunipa Dev, Tao Li, Jeff Phillips and Vivek Srikumar

AAAI, 2020.

Abstract

Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences in downstream models that rely on them. We use this observation to design a mechanism for measuring stereotypes using the task of natural language inference. We demonstrate a reduction in invalid inferences via bias mitigation strategies on static word embeddings (GloVe). Further, we show that for gender bias, these techniques extend to contextualized embeddings when applied selectively only to the static components of contextualized embeddings (ELMo, BERT).

Bib Entry

@inproceedings{dev2020on-measuring,
  author = {Dev, Sunipa and Li, Tao and Phillips, Jeff and Srikumar, Vivek},
  title = {{On Measuring and Mitigating Biased Inferences of Word Embeddings}},
  booktitle = {AAAI},
  year = {2020}
}

Vivek Srikumar