Abstract
Our submission to the CRAC 2023 shared task, described herein, is an adapted entity-ranking model jointly trained on all 17 datasets spanning 12 languages. Our model outperforms the shared task baselines by a difference in F1 score of +8.47, achieving an ultimate F1 score of 65.43 and fourth place in the shared task. We explore design decisions related to data preprocessing, the pretrained encoder, and data mixing.- Anthology ID:
- 2023.crac-sharedtask.5
- Volume:
- Proceedings of the CRAC 2023 Shared Task on Multilingual Coreference Resolution
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Zdeněk Žabokrtský, Maciej Ogrodniczuk
- Venues:
- CRAC | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 52–57
- Language:
- URL:
- https://aclanthology.org/2023.crac-sharedtask.5
- DOI:
- 10.18653/v1/2023.crac-sharedtask.5
- Cite (ACL):
- Ian Porada and Jackie Chi Kit Cheung. 2023. McGill at CRAC 2023: Multilingual Generalization of Entity-Ranking Coreference Resolution Models. In Proceedings of the CRAC 2023 Shared Task on Multilingual Coreference Resolution, pages 52–57, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- McGill at CRAC 2023: Multilingual Generalization of Entity-Ranking Coreference Resolution Models (Porada & Cheung, CRAC-WS 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.crac-sharedtask.5.pdf