Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional ways of formulating the commonsense question into a question-answering form or utilizing external knowledge to learn retrieval-based methods are less applicable in the open-ended setting due to an inherent challenge. Without pre-defining an answer scope or a few candidates, open-ended commonsense reasoning entails predicting answers by searching over an extremely large searching space. Moreover, most questions require implicit multi-hop reasoning, which presents even more challenges to our problem. In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision. The reasoning paths can help to identify the most precise answer to the commonsense question. We conduct experiments on two commonsense benchmark datasets. Compared to other approaches, our proposed method achieves better performance both quantitatively and qualitatively.

翻译：开放式常识推理定义为在解决常识问题时，既不提供1）少量候选答案列表，也不提供2）预定义的答案范围。由于固有的挑战性，将常识问题转化为问答形式或利用外部知识学习基于检索的方法等传统方式，在开放式场景下适用性较低。由于未预先定义答案范围或候选答案集，开放式常识推理需要通过在极大的搜索空间中检索来预测答案。此外，大多数问题需要隐式的多跳推理，这进一步增加了问题的难度。在本研究中，我们利用预训练语言模型在外部知识库上迭代检索推理路径，该方法无需任务特定的监督。这些推理路径有助于识别常识问题的最精确答案。我们在两个常识基准数据集上进行了实验。与其他方法相比，我们提出的方法在定量和定性评估中均实现了更优性能。