Exploring interpretability in event extraction: Multitask learning of a neural event classifier and an explanation decoder

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

6 Scopus citations

Abstract

We propose an interpretable approach for event extraction that mitigates the tension between generalization and interpretability by jointly training for the two goals. Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier. We evaluate the proposed approach on three biomedical events and show that the decoder generates interpretable rules that serve as accurate explanations for the event classifier’s decisions, and, importantly, that the joint training generally improves the performance of the event classifier. Lastly, we show that our approach can be used for semi-supervised learning, and that its performance improves when trained on automatically-labeled data generated by a rule-based system.

Original languageEnglish (US)
Title of host publicationACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages169-175
Number of pages7
ISBN (Electronic)9781952148033
StatePublished - 2020
Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Student Research Workshop, SRW 2020 - Virtual, Online, United States
Duration: Jul 5 2020Jul 10 2020

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Student Research Workshop, SRW 2020
Country/TerritoryUnited States
CityVirtual, Online
Period7/5/207/10/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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