  Existing pre-training methods for extractive Question Answering (QA) generate
cloze-like queries different from natural questions in syntax structure, which
could overfit pre-trained models to simple keyword matching. In order to
address this problem, we propose a novel Momentum Contrastive pRe-training fOr
queStion anSwering (MCROSS) method for extractive QA. Specifically, MCROSS
introduces a momentum contrastive learning framework to align the answer
probability between cloze-like and natural query-passage sample pairs. Hence,
the pre-trained models can better transfer the knowledge learned in cloze-like
samples to answering natural questions. Experimental results on three
benchmarking QA datasets show that our method achieves noticeable improvement
compared with all baselines in both supervised and zero-shot scenarios.
