@inproceedings{9708a855778f47cfb526527bf1b49e73,
title = "Cross-document Coreference Resolution over Predicted Mentions",
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.",
author = "Arie Cattan and Alon Eirew and Gabriel Stanovsky and Mandar Joshi and Ido Dagan",
note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 ; Conference date: 01-08-2021 Through 06-08-2021",
year = "2021",
language = "الإنجليزيّة",
series = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
publisher = "Association for Computational Linguistics (ACL)",
pages = "5100--5107",
editor = "Chengqing Zong and Fei Xia and Wenjie Li and Roberto Navigli",
booktitle = "Findings of the Association for Computational Linguistics",
address = "الولايات المتّحدة",
}