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.

本文提出了一种名为 Introspective Distillation（IntroD）的新型去偏差方法，通过内省训练样本是否适合现实 ID 世界或对立 OOO 世界来融合 OOD 和 ID 的归纳偏差，用于解决语言和阅读理解等领域中 QA 模型的数据偏差问题，并在 VQA v2、VQA-CP 和 SQuAD 数据集上进行了实验验证。