Identifying and Reducing Gender Bias in Word-Level Language Models

@article{Bordia2019IdentifyingAR,
  title={Identifying and Reducing Gender Bias in Word-Level Language Models},
  author={Shikha Bordia and Samuel R. Bowman},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.03035}
}
Many text corpora exhibit socially problematic biases, which can be propagated or amplified in the models trained on such data. For example, doctor cooccurs more frequently with male pronouns than female pronouns. In this study we (i) propose a metric to measure gender bias; (ii) measure bias in a text corpus and the text generated from a recurrent neural network language model trained on the text corpus; (iii) propose a regularization loss term for the language model that minimizes the… Expand
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