BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering
This addresses the problem of expensive dataset construction for commonsense reasoning, offering a more efficient approach for researchers and practitioners, though it is incremental as it builds on existing unsupervised methods.
The paper tackles unsupervised commonsense question answering by transforming multiple-choice tasks into binary classification, ranking candidate answers by reasonableness, and shows effectiveness on benchmarks while being less data-hungry than existing methods.
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.