BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering

• URL: http://arxiv.org/abs/2305.15932v2
• Date: Wed, 7 Jun 2023 20:33:09 GMT
• Title: BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering
• Authors: Jie He and Simon Chi Lok U and V\'ictor Guti\'errez-Basulto and Jeff Z. Pan
• Abstract summary: Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as the construction of commonsense reasoning datasets is expensive. 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.
• Score: 11.99004747630325
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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