2021

A Survey on Gender Bias in Natural Language Processing

Stanczak, Karolina, Augenstein, Isabelle

Understand

Language can be used as a means of reproducing and enforcing harmful stereotypes and biases and has been analysed as such in numerous research.

In this paper, we present a survey of 304 papers on gender bias in natural language processing.
We analyse definitions of gender and its categories within social sciences and connect them to formal definitions of gender bias in NLP research.
We survey lexica and datasets applied in research on gender bias and then compare and contrast approaches to detecting and mitigating gender bias.

Built on

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