End-to-end Multilingual Coreference Resolution with Headword Mention Representation
Proceedings of the Seventh Workshop on Computational Models of Reference, Anaphora and Coreference (2024)
Abstract
This paper describes our approach to the CRAC 2024 Shared Task on Multilingual Coreference Resolution. Our model is based on an endto-end coreference resolution system. Apart from joined multilingual training, we improved our results with headword mention representation and training large model mT5-xxl through LORA. We provide an analysis of the performance of our model. Our system ended up in 4th place. Moreover, we reached the best performance on three datasets out of 21.;
Authors
BibTex
@inproceedings{prazak-konopik-2024-end, title = "End-to-end Multilingual Coreference Resolution with Headword Mention Representation", author = "Prazak, Ondrej and Konop{\'i}k, Miloslav", editor = "Ogrodniczuk, Maciej and Nedoluzhko, Anna and Poesio, Massimo and Pradhan, Sameer and Ng, Vincent", booktitle = "Proceedings of the Seventh Workshop on Computational Models of Reference, Anaphora and Coreference", month = nov, year = "2024", address = "Miami", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.crac-1.10/", doi = "10.18653/v1/2024.crac-1.10", pages = "107--113" }
