  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.
