Recently, end-to-end trained models for multiple-choice commonsense question answering (QA) have delivered promising results. However, such question-answering systems cannot be directly applied in real-world scenarios where answer candidates are not provided. Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices. On the OpenCSR challenge set, many questions require implicit multi-hop reasoning and have a large decision space, reflecting the difficult nature of this task. Existing work on OpenCSR sorely focuses on improving the retrieval process, which extracts relevant factual sentences from a textual knowledge base, leaving the important and non-trivial reasoning task outside the scope. In this work, we extend the scope to include a reasoner that constructs a question-dependent open knowledge graph based on retrieved supporting facts and employs a sequential subgraph reasoning process to predict the answer. The subgraph can be seen as a concise and compact graphical explanation of the prediction. Experiments on two OpenCSR datasets show that the proposed model achieves great performance on benchmark OpenCSR datasets.

翻译：近期，用于多项选择常识问答（QA）的端到端训练模型取得了令人瞩目的成果。然而，此类问答系统无法直接应用于未提供候选答案的真实场景。为此，近期发布了一项新的基准挑战集——开放式常识推理（OpenCSR），其中包含无预定义选项的自然科学问题。在OpenCSR挑战集中，许多问题需要隐式的多跳推理，且决策空间较大，体现了该任务的复杂本质。现有关于OpenCSR的研究主要聚焦于改进检索流程（即从文本知识库中提取相关事实句子），而将重要且非平凡的推理任务排除在范畴之外。本研究将范畴扩展至包含一个推理器：该推理器基于检索到的支持事实构建问题相关的开放知识图谱，并采用顺序子图推理过程来预测答案。该子图可视为预测结果的简洁紧凑图形化解释。在两个OpenCSR数据集上的实验表明，所提出的模型在基准OpenCSR数据集上取得了优异性能。