Findings of the shared task on multilingual coreference resolution
CRAC (2022)
Research topics:
Neural NetworksAbstract
This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop trainable systems capable of identifying mentions and clustering them according to identity coreference. The public edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data. The CoNLL score used in previous coreference-oriented shared tasks was used as the main evaluation metric. There were 8 coreference prediction systems submitted by 5 participating teams; in addition, there was a competitive Transformer-based baseline system provided by the organizers at the beginning of the shared task. The winner system outperformed the baseline by 12 percentage points (in terms of the CoNLL scores averaged across all datasets for individual languages).
Authors
BibTex
@inproceedings{zabokrtsky-etal-2022-findings, title = "Findings of the Shared Task on Multilingual Coreference Resolution", author = "{\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k and Konop{\'\i}k, Miloslav and Nedoluzhko, Anna and Nov{\'a}k, Michal and Ogrodniczuk, Maciej and Popel, Martin and Pra{\v{z}}{\'a}k, Ond{\v{r}}ej and Sido, Jakub and Zeman, Daniel and Zhu, Yilun", 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.1", pages = "1--17", abstract = "This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop trainable systems capable of identifying mentions and clustering them according to identity coreference. The public edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data. The CoNLL score used in previous coreference-oriented shared tasks was used as the main evaluation metric. There were 8 coreference prediction systems submitted by 5 participating teams; in addition, there was a competitive Transformer-based baseline system provided by the organizers at the beginning of the shared task. The winner system outperformed the baseline by 12 percentage points (in terms of the CoNLL scores averaged across all datasets for individual languages).", }
