  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.
