Abstract
As Natural Language Processing (NLP) and
Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models
have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While
the study of bias in artificial intelligence is not
new, methods to mitigate gender bias in NLP
are relatively nascent. In this paper, we review
contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender
bias based on four forms of representation bias
and analyze methods recognizing gender bias.
Furthermore, we discuss the advantages and
drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.