End-to-end Multilingual Coreference Resolution with Mention Head Prediction

Ondřej Pražák, Miloslav Konopik


Abstract
This paper describes our approach to the CRAC 2022 Shared Task on Multilingual Coreference Resolution. Our model is based on a state-of-the-art end-to-end coreference resolution system. Apart from joined multilingual training, we improved our results with mention head prediction. We also tried to integrate dependency information into our model. Our system ended up in third place. Moreover, we reached the best performance on two datasets out of 13.
Anthology ID:
2022.crac-mcr.3
Volume:
Proceedings of the CRAC 2022 Shared Task on Multilingual Coreference Resolution
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Zdeněk Žabokrtský, Maciej Ogrodniczuk
Venue:
CRAC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
23–27
Language:
URL:
https://aclanthology.org/2022.crac-mcr.3
DOI:
Bibkey:
Cite (ACL):
Ondřej Pražák and Miloslav Konopik. 2022. End-to-end Multilingual Coreference Resolution with Mention Head Prediction. In Proceedings of the CRAC 2022 Shared Task on Multilingual Coreference Resolution, pages 23–27, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
End-to-end Multilingual Coreference Resolution with Mention Head Prediction (Pražák & Konopik, CRAC 2022)
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PDF:
https://aclanthology.org/2022.crac-mcr.3.pdf