I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning

Jungwoo Lim, Dongsuk Oh, Yoonna Jang, Kisu Yang, Heuiseok Lim

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

17 Citations (Scopus)

Abstract

CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation of the question. To shed light upon the semantic interpretation of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graph is pruned from a full integrated graph encompassing Abstract Meaning Representation (AMR) graph generated from input questions and an external commonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploited to interpret the reasoning path as well as to predict the correct answer on the CommonsenseQA task. This paper presents the manner in which the commonsense reasoning process can be interpreted with the relations and concepts provided by the ACP graph. Moreover, ACP-based models are shown to outperform the baselines.

Original languageEnglish
Title of host publicationCOLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference
EditorsDonia Scott, Nuria Bel, Chengqing Zong
PublisherAssociation for Computational Linguistics (ACL)
Pages2459-2471
Number of pages13
ISBN (Electronic)9781952148279
Publication statusPublished - 2020
Event28th International Conference on Computational Linguistics, COLING 2020 - Virtual, Online, Spain
Duration: 2020 Dec 82020 Dec 13

Publication series

NameCOLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference

Conference

Conference28th International Conference on Computational Linguistics, COLING 2020
Country/TerritorySpain
CityVirtual, Online
Period20/12/820/12/13

Bibliographical note

Funding Information:
This work was supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques). Also, this research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2018-0-01405) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation)

Publisher Copyright:
© 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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