• Corpus ID: 240354576

Introspective Distillation for Robust Question Answering

@inproceedings{Niu2021IntrospectiveDF,
  title={Introspective Distillation for Robust Question Answering},
  author={Yulei Niu and Hanwang Zhang},
  booktitle={Neural Information Processing Systems},
  year={2021}
}
Question answering (QA) models are well-known to exploit data bias, e.g., the language prior in visual QA and the position bias in reading comprehension. Recent debiasing methods achieve good out-of-distribution (OOD) generalizability with a considerable sacrifice of the in-distribution (ID) performance. Therefore, they are only applicable in domains where the test distribution is known in advance. In this paper, we present a novel debiasing method called Introspective Distillation (IntroD) to… 

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