Before revision:  In this paper, we propose to learn a multi-hop knowledge path generatorto generate structured evidence dynamically according to the questions. Our generator uses a pre-trained language modelas the backbone, leveraging a large amount of unstructured knowledge stored in the language model to supplement the incompleteness of the knowledge base. The experiments on two commonsense QA datasets demonstrate the effectiveness of our method , which improves over strong baselines significantly and also provides human interpretableexplanations for the predictions .
After revision:  In this paper, we propose to learn a 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 , which improves over strong baselines significantly and also provides human interpretableexplanations for the predictions .