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：在未提供1）少量候选答案列表和2）预定义答案范围的情况下，解决常识性问题。将常识性问题表述为问答形式或利用外部知识学习基于检索的方法的传统方式，由于存在固有挑战，在开放式场景下难以适用。在未预定义答案范围或少量候选答案的条件下，开放式常识推理需要通过在极其庞大的搜索空间中搜索来预测答案。此外，大多数问题隐含多跳推理需求，这给我们的问题带来了更大挑战。本研究利用预训练语言模型，在外部知识库上迭代式检索推理路径，该方法无需任务特定的监督信号。这些推理路径有助于识别常识性问题的最精确答案。我们在两个常识基准数据集上进行实验。与其他方法相比，我们提出的方法在定量和定性评估中均取得了更优性能。