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Mitigating Gender Bias in Natural Language Processing: Literature Review

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Figure

Figure 1: Observation and evaluation of gender bias inNLP. Bias observation occurs in both the training setsand the test sets specifically for evaluating the genderbias of a given algorithm’s predictions
Table 1: Following the talk by Crawford (2017), we categorize representation bias in NLP tasks into the followingfour categories: (D)enigration, (S)tereotyping, (R)ecognition, (U)nder-representation.
Table 2: Summary of GBETs. GBETs evaluate models trained for specific tasks for gender bias
Table 3: Debiasing methods can be categorized ac-cording to how they affect the model
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