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Cross-document Event Coreference Search: Task, Dataset and Modeling

  • Intel Labs

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

7 Scopus citations

Abstract

The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task - Cross-document Coreference Search, focusing in this paper on event coreference. Concretely, given a mention in context of an event of interest, considered as a query, the task is to find all coreferring mentions for the query event in a large document collection. To support research on this task, we create a corresponding dataset, which is derived from Wikipedia while leveraging annotations in the available Wikipedia Event Coreference dataset (WEC-Eng). Observing that the coreference search setup is largely analogous to the setting of Open Domain Question Answering, we adapt the prominent Deep Passage Retrieval (DPR) model to our setting, as an appealing baseline. Finally, we present a novel model that integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.

Original languageEnglish
Title of host publicationProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022
EditorsYoav Goldberg, Zornitsa Kozareva, Yue Zhang
PublisherAssociation for Computational Linguistics (ACL)
Pages900-913
Number of pages14
ISBN (Electronic)9781959429401
DOIs
StatePublished - 2022
Event2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 - Hybrid, Abu Dhabi, United Arab Emirates
Duration: 7 Dec 202211 Dec 2022

Publication series

NameProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

Conference

Conference2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022
Country/TerritoryUnited Arab Emirates
CityHybrid, Abu Dhabi
Period7/12/2211/12/22

Bibliographical note

Publisher Copyright:
© 2022 Association for Computational Linguistics.

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