To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning
This addresses the challenge for NLP systems in distinguishing subtle literal changes that make questions unanswerable, representing an incremental improvement in a specific domain.
The paper tackled the problem of machine reading comprehension with unanswerable questions by proposing a span-based contrastive learning method, which improved baseline models by 0.86-2.14 absolute EM on the SQuAD 2.0 dataset.
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.