End-to-end Multilingual Coreference Resolution with Mention Head Prediction
CRAC (2022)
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 the third place. Moreover, we reached the best performance on two datasets out of 13.
Authors
BibTex
@inproceedings{prazak-konopik-2022-end, title = "End-to-end Multilingual Coreference Resolution with Mention Head Prediction", author = "Pra{\v{z}}{\'a}k, Ond{\v{r}}ej and Konopik, Miloslav", booktitle = "Proceedings of the CRAC 2022 Shared Task on Multilingual Coreference Resolution", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.crac-mcr.3", pages = "23--27", 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.", }
