To Answer or Not To Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning (2022)

Abstract: 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.

## introduction machine reading comprehension (mrc) is an important task in natural language @xcite , aiming to answer specific questions through scanning a given passage @xcite @xcite . as a fundamental nlu task, mrc also plays an essential role in many applications such as question answering and dialogue tasks @xcite @xcite . with the rapid development of pre-trained language models (plms), there is also a paradigm shift @xcite @xcite reformulating other nlp tasks (e.g. information extraction) into mrc format, especially for open-domain scenarios @xcite . in most of the application scenarios, there exists a hypothesis that only answerable questions can be asked, which is somehow unrealistic and unreasonable. thus, the model that is capable of distinguish-passage: the legend of zelda: twilight princess (japanese: ゼルダの伝説 トワイライトプリンセス, hepburn: zeruda no densetsu: towairaito purinsesu?) is an action-adventure game developed and published by nintendo for the gamecube and wii home video game consoles. it is the thirteenth installment in the the legend of zelda series. originally planned for release on the ## approach in this section, we first introduce the task of machine reading comprehension with unanswerable questions (mrc-u). then, a baseline mrc model based on plm is described. at last, we propose a span-based contrastive learning method for mrc-u, named as spancl. in this paper, question paraphrase and positive question, question distortion and negative question are used interchangeably. ## conclusion in this paper, we propose a span-based method of contrastive learning (spancl) to solve the mrc task with unanswerable questions. spancl is devised based on the fact that an answerable question can become unanswerable with slight literal changes. by explicitly contrasting an answerable question with its paraphrase and distortion at the answer span level, mrc models can be taught to perceive subtle but crucial literal changes. experi-mental results demonstrate that spancl is modelagnostic and can improve mrc models significantly. additional experiments show that spancl is more effective to utilize the generated questions than other methods. in addition, it should be noticed that how to generate high-quality question examples is not fully investigated in this paper, which may introduce a performance bottleneck to spancl. therefore, a study on question generation compatible with spancl is encouraged in the future.