Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. The framework first grounds a question-answer pair from the semantic space to the knowledge-based symbolic space as a schema graph, a related sub-graph of external knowledge graphs. It represents schema graphs with a novel knowledge-aware graph network module named KagNet, and finally scores answers with graph representations. Our model is based on graph convolutional networks and LSTMs, with a hierarchical path-based attention mechanism. The intermediate attention scores make it transparent and interpretable, which thus produce trustworthy inferences. Using ConceptNet as the only external resource for Bert-based models, we achieved state-of-the-art performance on the CommonsenseQA, a large-scale dataset for commonsense reasoning.

翻译：常识推理旨在赋予人类能力对日常生活中的普通情况作出推定的机器以权力。在本文中,我们提议了一个用于回答常识问题的文字推理框架,该框架有效利用外部结构化的常识知识图表来进行可以解释的推理。框架首先将语义空间的问答配对作为基于知识的象征空间的系统图,一个相关的外部知识图子图。它代表了带有名为KagNet的新颖知识觉察图网络模块的系统图,最后以图表表示的评分答案。我们的模型以图表相联网络和LSTMs为基础,以分级路径关注机制为基础。中间的计分数使得它具有透明度和可解释性,从而产生可信赖的推理。用概念网作为以伯特为基础的模型的唯一外部资源,我们在ComesenseQA上实现了最先进的表现,这是用于常识推理的大规模数据集。