@inproceedings{e332f726fe8f4a1c8f574e105f3871ef,
title = "Paraphrasing vs coreferring: Two sides of the same coin",
abstract = "We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution. First, we used annotations from an event coreference dataset as distant supervision to re-score heuristically-extracted predicate paraphrases. The new scoring gained more than 18 points in average precision upon their ranking by the original scoring method. Then, we used the same re-ranking features as additional inputs to a state-of-the-art event coreference resolution model, which yielded modest but consistent improvements to the model{\textquoteright}s performance. The results suggest a promising direction to leverage data and models for each of the tasks to the benefit of the other.",
author = "Yehudit Meged and Avi Caciularu and Vered Shwartz and Ido Dagan",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics; Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 ; Conference date: 16-11-2020 Through 20-11-2020",
year = "2020",
language = "الإنجليزيّة",
series = "Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020",
publisher = "Association for Computational Linguistics (ACL)",
pages = "4897--4907",
booktitle = "Findings of the Association for Computational Linguistics Findings of ACL",
address = "الولايات المتّحدة",
}