A Relation Embedding Assistance Networks for Multi-hop Question Answering
Authors: 
Songlin Jiao, Zhenfang Zhu, Jiangtao Qi, Fuyong Xu, Hongli Pei, Wenling Wang, Ze Song, Peiyu LiuAuthors Info & Claims
Songlin Jiao, Zhenfang Zhu, Jiangtao Qi, Fuyong Xu, Hongli Pei, Wenling Wang, Ze Song, Peiyu LiuAuthors Info & ClaimsArticle No.: 26, Pages 1 - 17
Abstract
Multi-hop Knowledge Graph Question Answering aims at finding an entity to answer natural language questions from knowledge graphs. When humans perform multi-hop reasoning, people tend to focus on specific relations across different hops and confirm the next entity. Therefore, most algorithms choose the wrong specific relation, which makes the system deviate from the correct reasoning path. The specific relation at each hop plays an important role in multi-hop question answering. Existing work mainly relies on the question representation as relation information, which cannot accurately calculate the specific relation distribution. In this article, we propose an interpretable assistance framework that fully utilizes the relation embeddings to assist in calculating relation distributions at each hop. Moreover, we employ the fusion attention mechanism to ensure the integrity of relation information and hence to enrich the relation embeddings. The experimental results on three English datasets and one Chinese dataset demonstrate that our method significantly outperforms all baselines. The source code of REAN will be available at https://github.com/2399240664/REAN
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February 2024
340 pages
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Publication History
Published: 08 February 2024
Online AM: 15 December 2023
Accepted: 20 November 2023
Revised: 11 August 2023
Received: 07 November 2022
Published in TALLIP Volume 23, Issue 2
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