LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution

Shon Otmazgin, Arie Cattan, Yoav Goldberg


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.
Anthology ID:
2023.eacl-main.202
Volume:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2752–2760
Language:
URL:
https://aclanthology.org/2023.eacl-main.202
DOI:
10.18653/v1/2023.eacl-main.202
Bibkey:
Cite (ACL):
Shon Otmazgin, Arie Cattan, and Yoav Goldberg. 2023. LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2752–2760, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution (Otmazgin et al., EACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.eacl-main.202.pdf
Video:
 https://aclanthology.org/2023.eacl-main.202.mp4