@inproceedings{2912f2c28c1c4d12b020f26ddd40e247,
title = "LINGMESS: Linguistically Informed Multi Expert Scorers for Coreference Resolution",
abstract = "Current state-of-the-art coreference systems are based on a single pairwise scoring component, which assigns to each pair of mention spans a score reflecting their tendency to core-fer to each other. We observe that different kinds of mention pairs require different information sources to assess their score. We present LINGMESS, a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring function for each category. This significantly improves the accuracy of the pairwise scorer as well as of the overall coreference performance on the English Ontonotes coreference corpus and 5 additional datasets.",
author = "Shon Otmazgin and Arie Cattan and Yoav Goldberg",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 ; Conference date: 02-05-2023 Through 06-05-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.18653/v1/2023.eacl-main.202",
language = "English",
series = "EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "2744--2752",
booktitle = "EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference",
address = "United States",
}