  Standard accuracy metrics indicate that modern reading comprehension systems
have achieved strong performance in many question answering datasets. However,
the extent these systems truly understand language remains unknown, and
existing systems are not good at distinguishing distractor sentences, which
look related but do not actually answer the question. To address this problem,
we propose QAInfomax as a regularizer in reading comprehension systems by
maximizing mutual information among passages, a question, and its answer.
QAInfomax helps regularize the model to not simply learn the superficial
correlation for answering questions. The experiments show that our proposed
QAInfomax achieves the state-of-the-art performance on the benchmark
Adversarial-SQuAD dataset.
