EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction

Qi Zeng, Qiusi Zhan, Heng Ji

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

Abstract

Events are inter-related in documents. Motivated by the one-sense-per-discourse theory, we hypothesize that a participant tends to play consistent roles across multiple events in the same document. However recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events, which will further cause discrepancy for downstream applications such as event knowledge base population, question answering, and hypothesis generation. In this work, we formulate event argument consistency as the constraints from event-event relations under the document-level setting. To improve consistency we introduce the Event-Aware Argument Extraction (EA2E) model with augmented context for training and inference. Experiment results on WIKIEVENTS and ACE2005 datasets demonstrate the effectiveness of EA2E compared to baseline methods.

Original languageEnglish (US)
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationNAACL 2022 - Findings
PublisherAssociation for Computational Linguistics (ACL)
Pages2649-2655
Number of pages7
ISBN (Electronic)9781955917766
DOIs
StatePublished - 2022
Event2022 Findings of the Association for Computational Linguistics: NAACL 2022 - Seattle, United States
Duration: Jul 10 2022Jul 15 2022

Publication series

NameFindings of the Association for Computational Linguistics: NAACL 2022 - Findings

Conference

Conference2022 Findings of the Association for Computational Linguistics: NAACL 2022
Country/TerritoryUnited States
CitySeattle
Period7/10/227/15/22

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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