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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.crac-mcr.3.pdf