  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 make the best of both
worlds for QA. Our key technical contribution is to blend the inductive bias of
OOD and ID by introspecting whether a training sample fits in the factual ID
world or the counterfactual OOD one. Experiments on visual QA datasets VQA v2,
VQA-CP, and reading comprehension dataset SQuAD demonstrate that our proposed
IntroD maintains the competitive OOD performance compared to other debiasing
methods, while sacrificing little or even achieving better ID performance
compared to the non-debiasing ones.
