  Machine Reading Comprehension with Unanswerable Questions is a difficult NLP
task, challenged by the questions which can not be answered from passages. It
is observed that subtle literal changes often make an answerable question
unanswerable, however, most MRC models fail to recognize such changes. To
address this problem, in this paper, we propose a span-based method of
Contrastive Learning (spanCL) which explicitly contrast answerable questions
with their answerable and unanswerable counterparts at the answer span level.
With spanCL, MRC models are forced to perceive crucial semantic changes from
slight literal differences. Experiments on SQuAD 2.0 dataset show that spanCL
can improve baselines significantly, yielding 0.86-2.14 absolute EM
improvements. Additional experiments also show that spanCL is an effective way
to utilize generated questions.
