Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering

Zhang, Qin, Chen, Shangsi, Fang, Meng and Chen, Xiaojun (2023) Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering. INFORMATION PROCESSING & MANAGEMENT, 60 (3). p. 103297.

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Abstract

Humans are able to reason from multiple sources to arrive at the correct answer. In the context of Multiple Choice Question Answering (MCQA), knowledge graphs can provide subgraphs based on different combinations of questions and answers, mimicking the way humans find answers. However, current research mainly focuses on independent reasoning on a single graph for each question–answer pair, lacking the ability for joint reasoning among all answer candidates. In this paper, we propose a novel method KMSQA, which leverages multiple subgraphs from the large knowledge graph ConceptNet to model the comprehensive reasoning process. We further encode the knowledge graphs with shared Graph Neural Networks (GNNs) and perform joint reasoning across multiple subgraphs. We evaluate our model on two common datasets: CommonsenseQA (CSQA) and OpenBookQA (OBQA). Our method achieves an exact match score of 74.53% on CSQA and 71.80% on OBQA, outperforming all eight baselines.

Item Type:	Article
Uncontrolled Keywords:	Joint reasoning, Multi-choice question answering, Multiple knowledge graphs
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	27 Mar 2023 07:47
Last Modified:	10 Feb 2024 02:30
DOI:	10.1016/j.ipm.2023.103297
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169245