@inproceedings{otmazgin-etal-2023-lingmess,
title = "{L}ing{M}ess: Linguistically Informed Multi Expert Scorers for Coreference Resolution",
author = "Otmazgin, Shon and
Cattan, Arie and
Goldberg, Yoav",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.202/",
doi = "10.18653/v1/2023.eacl-main.202",
pages = "2752--2760",
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 corefer 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."
}
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%0 Conference Proceedings
%T LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
%A Otmazgin, Shon
%A Cattan, Arie
%A Goldberg, Yoav
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F otmazgin-etal-2023-lingmess
%X 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 corefer 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.
%R 10.18653/v1/2023.eacl-main.202
%U https://aclanthology.org/2023.eacl-main.202/
%U https://doi.org/10.18653/v1/2023.eacl-main.202
%P 2752-2760
Markdown (Informal)
[LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://aclanthology.org/2023.eacl-main.202/) (Otmazgin et al., EACL 2023)
ACL