BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering

Jie He, Simon Chi Lok U, ‪Víctor Gutiérrez-Basulto, Jeff Z Pan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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
Original languageEnglish
Title of host publicationProceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 2: Short Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages376-387
Volume2
Publication statusPublished - 9 Jul 2023
Event61st Annual Meeting of the Association for Computational Linguistics - Toronto, Canada
Duration: 9 Jul 202314 Jul 2023
Conference number: 61
https://2023.aclweb.org/

Conference

Conference61st Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2023
Country/TerritoryCanada
CityToronto
Period9/07/2314/07/23
Internet address

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