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Commonsense question answering (CQA) aims to test if models can answerquestions regarding commonsense knowledge that everyone knows. Prior works thatincorporate external knowledge bases have shown promising results, butknowledge bases are expensive to construct and are often limited to a fixed setof relations. In this paper, we instead focus on better utilizing the\textit{implicit knowledge} stored in pre-trained language models. Whileresearchers have found that the knowledge embedded in pre-trained languagemodels can be extracted by having them fill in the blanks of carefully designedprompts for relation extraction and text classification, it remains unclear ifwe can adopt this paradigm in CQA where the inputs and outputs take much moreflexible forms. To this end, we investigate four translation methods that cantranslate natural questions into cloze-style sentences to better solicitcommonsense knowledge from language models, including a syntactic-based model,an unsupervised neural model, and two supervised neural models. In addition, tocombine the different translation methods, we propose to encourage consistencyamong model predictions on different translated questions with unlabeled data.We demonstrate the effectiveness of our methods on three CQA datasets inzero-shot settings. We show that our methods are complementary to a knowledgebase improved model, and combining them can lead to state-of-the-art zero-shotperformance. Analyses also reveal distinct characteristics of the differentcloze translation methods and provide insights on why combining them can leadto great improvements.

Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization