ArT: All-round Thinker for Unsupervised Commonsense Question-Answering

Without labeled question-answer pairs for necessary training, unsupervised commonsense question-answering (QA) appears to be extremely challenging due to its indispensable unique prerequisite on commonsense source like knowledge bases (KBs), which are usually highly resource consuming in construction. Recently pre-trained language models (PrLMs) show effectiveness as an alternative for commonsense clues when they play a role of knowledge generator. However, existing work simply generates hundreds of pseudo-answers, or roughly performs knowledge generation according to templates once for all, which may result in much noise and thus hinders the quality of generated knowledge. Motivated by human thinking experience, we propose an approach of All-round Thinker (ArT) by fully taking association during knowledge generating. In detail, our model first focuses on key parts in the given context, and then generates highly related knowledge on such a basis in an association way like human thinking. Besides, for casual reasoning, a reverse thinking mechanism is proposed to conduct bidirectional inferring between cause and effect. ArT is totally unsupervised and KBs-free. We evaluate it on three commonsense QA benchmarks: COPA, SocialIQA and SCT. On all scales of PrLM backbones, ArT shows its brilliant performance and outperforms previous advanced unsupervised models.