Corpus ID: 222141056

UnQovering Stereotyping Biases via Underspecified Questions

@article{Li2020UnQoveringSB,
  title={UnQovering Stereotyping Biases via Underspecified Questions},
  author={Tao Li and Daniel Khashabi and Tushar Khot and Ashish Sabharwal and V. Srikumar},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.02428}
}

Tao Li, Daniel Khashabi, +2 authors V. Srikumar

Published 2020

Computer Science

ArXiv

Warning: This paper contains examples of stereotypes that are potentially offensive. While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored. We present UNQOVER, a general framework to probe and quantify biases through underspecified questions. We show that a naı̈ve use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors: positional dependence and question… CONTINUE READING

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