Dynamic Multi-hop Reasoning

Liang Xu, Junjie Yao*, Yingjie Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Multi-hop reasoning is an essential part of the current reading comprehension and question answering areas. The reasoning methods have been extensively studied, and most of them are generally focused on the pre-retrieval based inference, with the help of a few paragraphs. These methods are fixed and unable to cope with dynamic and complex questions. Here, we propose to utilize the dynamic graph reasoning network for multi-hop reading comprehension question answering. Specifically, the new approach continuously infers the clue entities and candidate answers based on the question and clue paragraphs. The clue entities and candidate answers extracted at each hop are used as new nodes to expand the dynamic graph. Then we iteratively update the semantic representation of the questions via dynamic question memory, and apply the graph attention network to encode the information of inference paths. Extensive experiments on two datasets verify the advantage and improvements of the proposed approach.

Original languageEnglish
Title of host publicationWeb and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Proceedings
EditorsXin Wang, Rui Zhang, Young-Koo Lee, Le Sun, Yang-Sae Moon
PublisherSpringer Science and Business Media Deutschland GmbH
Pages535-543
Number of pages9
ISBN (Print)9783030602581
DOIs
StatePublished - 2020
Event4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020 - Tianjin, China
Duration: 18 Sep 202020 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12317 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020
Country/TerritoryChina
CityTianjin
Period18/09/2020/09/20

Keywords

  • Commonsense
  • Graph attention network
  • Multi-hop reasoning

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