Cross-document Coreference Resolution over Predicted Mentions

Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan

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


Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models. However, the more challenging task of cross-document (CD) coreference resolution remained relatively under-explored, with the few recent models applied only to gold mentions. Here, we introduce the first end-to-end model for CD coreference resolution from raw text, which extends the prominent model for within-document coreference to the CD setting. Our model achieves competitive results for event and entity coreference resolution on gold mentions. More importantly, we set first baseline results, on the standard ECB+ dataset, for CD coreference resolution over predicted mentions. Further, our model is simpler and more efficient than recent CD coreference resolution systems, while not using any external resources.
Original languageAmerican English
Title of host publicationFindings of the Association for Computational Linguistics:
Subtitle of host publicationACL-IJCNLP 2021
Editors Roberto Navigli , Wenjie Li, Fei Xia, Chengqing Zong
PublisherAssociation for Computational Linguistics
Number of pages8
StatePublished - 1 Aug 2021

Publication series

NameACL Anthology


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