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Open-ended Commonsense Reasoning is defined as solving a commonsense questionwithout providing 1) a short list of answer candidates and 2) a pre-definedanswer scope. Conventional ways of formulating the commonsense question into aquestion-answering form or utilizing external knowledge to learnretrieval-based methods are less applicable in the open-ended setting due to aninherent challenge. Without pre-defining an answer scope or a few candidates,open-ended commonsense reasoning entails predicting answers by searching overan extremely large searching space. Moreover, most questions require implicitmulti-hop reasoning, which presents even more challenges to our problem. Inthis work, we leverage pre-trained language models to iteratively retrievereasoning paths on the external knowledge base, which does not requiretask-specific supervision. The reasoning paths can help to identify the mostprecise answer to the commonsense question. We conduct experiments on twocommonsense benchmark datasets. Compared to other approaches, our proposedmethod achieves better performance both quantitatively and qualitatively.

Open-ended Commonsense Reasoning with Unrestricted Answer Scope