Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as the construction of commonsense reasoning datasets is expensive, and they are inevitably limited in their scope. A popular approach to UCR is to fine-tune language models with external knowledge (e.g., knowledge graphs), but this usually requires a large number of training examples. In this paper, we propose to transform the downstream multiple choice question answering task into a simpler binary classification task by ranking all candidate answers according to their reasonableness. To this end, for training the model, we convert the knowledge graph triples into reasonable and unreasonable texts. Extensive experimental results show the effectiveness of our approach on various multiple choice question answering benchmarks. Furthermore, compared with existing UCR approaches using KGs, ours is less data hungry. Our code is available at https://github.com/probe2/BUCA.

本文提出将多项选择题回答任务转换为二元分类任务的新方法， 通过构建知识图谱三元组的合理和不合理文本，将其用于模型培训并进行候选答案排序。与现有知识图谱方法相比，所提出的方法对数据要求较少，在多项选择题回答数据集上表现良好。

BUCA：一种二进制分类方法用于无监督常识问答