Abstract
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 language | English |
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| Title of host publication | Findings of the Association for Computational Linguistics |
| Subtitle of host publication | ACL-IJCNLP 2021 |
| Editors | Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 5100-5107 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781954085541 |
| State | Published - 2021 |
| Event | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 - Virtual, Online Duration: 1 Aug 2021 → 6 Aug 2021 |
Publication series
| Name | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 |
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Conference
| Conference | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 |
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| City | Virtual, Online |
| Period | 1/08/21 → 6/08/21 |
Bibliographical note
Publisher Copyright:© 2021 Association for Computational Linguistics.