Corpus ID: 216553696

Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias

@article{Vig2020CausalMA,
  title={Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias},
  author={J. Vig and Sebastian Gehrmann and Yonatan Belinkov and Sharon Qian and Daniel Nevo and Y. Singer and S. Shieber},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.12265}
}
  • J. Vig, Sebastian Gehrmann, +4 authors S. Shieber
  • Published 2020
  • Computer Science
  • ArXiv
  • Common methods for interpreting neural models in natural language processing typically examine either their structure or their behavior, but not both. We propose a methodology grounded in the theory of causal mediation analysis for interpreting which parts of a model are causally implicated in its behavior. It enables us to analyze the mechanisms by which information flows from input to output through various model components, known as mediators. We apply this methodology to analyze gender bias… CONTINUE READING
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