Commonsense question answering (QA) requires background knowledge which is
not explicitly stated in a given context. Prior works use commonsense knowledge
graphs (KGs) to obtain this knowledge for reasoning. However, relying entirely
on these KGs may not suffice, considering their limited coverage and the
contextual dependence of their knowledge. In this paper, we augment a general
commonsense QA framework with a knowledgeable path generator. By extrapolating
over existing paths in a KG with a state-of-the-art language model, our
generator learns to connect a pair of entities in text with a dynamic, and
potentially novel, multi-hop relational path. Such paths can provide structured
evidence for solving commonsense questions without fine-tuning the path
generator. Experiments on two datasets show the superiority of our method over
previous works which fully rely on knowledge from KGs (with up to 6%
improvement in accuracy), across various amounts of training data. Further
evaluation suggests that the generated paths are typically interpretable,
novel, and relevant to the task.

本文提出了使用具有知识的路径生成器增强普通常识问题回答框架的方法，并使用现有路径和最先进的语言模型外推，生成连接文本中一对实体的动态，可能是新颖的多跳关系路径，从而提供解决常识问题所需的结构化证据。两个数据集上的实验表明，我们的方法优于之前完全依赖 KG 知识的研究（精度提高了高达 6%），而且生成的路径通常是可解释的、新颖的，也与任务相关。