  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.
