Commonsense question answering (QA) research requires machines to answer questions based on commonsense knowledge. However, this research requires expensive labor costs to annotate data as the basis of research, and models that rely on fine-tuning paradigms only apply to specific tasks, rather than learn a general commonsense reasoning ability. As a more robust method, zero-shot commonsense question answering shows a good prospect. The current zero-shot framework tries to convert triples in commonsense knowledge graphs (KGs) into QA-form samples as the pre-trained data source to incorporate commonsense knowledge into the model. However, this method ignores the multi-hop relationship in the KG, which is also an important central problem in commonsense reasoning. In this paper, we propose a novel multi-hop commonsense knowledge injection framework. Specifically, it explores multi-hop reasoning paradigm in KGs that conform to linguistic logic, and we further propose two multi-hop QA generation methods based on KGs. Then, we utilize contrastive learning to pre-train the model with the synthetic QA dataset to inject multi-hop commonsense knowledge. Extensive experiments on five commonsense question answering benchmarks demonstrate that our framework achieves state-of-art performance.

翻译：常识问答研究要求机器基于常识知识回答问题。然而，这类研究需要高昂的人工标注成本作为研究基础，且依赖微调范式的模型仅适用于特定任务，而非学习通用的常识推理能力。作为一种更具鲁棒性的方法，零跳常识问答展现出良好前景。现有零跳框架尝试将常识知识图谱中的三元组转化为问答形式样本作为预训练数据源，以将常识知识融入模型。但该方法忽略了知识图谱中的多跳关系，而这一关系恰是常识推理的核心问题。本文提出一种新颖的多跳常识知识注入框架：具体而言，该框架探索知识图谱中符合语言逻辑的多跳推理范式，并进一步提出两种基于知识图谱的多跳问答生成方法。随后，我们利用对比学习基于合成问答数据集对模型进行预训练，以注入多跳常识知识。在五个常识问答基准上的大量实验表明，我们的框架达到了最优性能。