  In spite of great advancements of machine reading comprehension (RC),
existing RC models are still vulnerable and not robust to different types of
adversarial examples. Neural models over-confidently predict wrong answers to
semantic different adversarial examples, while over-sensitively predict wrong
answers to semantic equivalent adversarial examples. Existing methods which
improve the robustness of such neural models merely mitigate one of the two
issues but ignore the other. In this paper, we address the over-confidence
issue and the over-sensitivity issue existing in current RC models
simultaneously with the help of external linguistic knowledge. We first
incorporate external knowledge to impose different linguistic constraints
(entity constraint, lexical constraint, and predicate constraint), and then
regularize RC models through posterior regularization. Linguistic constraints
induce more reasonable predictions for both semantic different and semantic
equivalent adversarial examples, and posterior regularization provides an
effective mechanism to incorporate these constraints. Our method can be applied
to any existing neural RC models including state-of-the-art BERT models.
Extensive experiments show that our method remarkably improves the robustness
of base RC models, and is better to cope with these two issues simultaneously.
