@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",
editor = "{\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k and
Ogrodniczuk, Maciej",
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).",
}
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<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).</abstract>
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%0 Conference Proceedings
%T Findings of the Shared Task on Multilingual Coreference Resolution
%A Žabokrtský, Zdeněk
%A Konopík, Miloslav
%A Nedoluzhko, Anna
%A Novák, Michal
%A Ogrodniczuk, Maciej
%A Popel, Martin
%A Pražák, Ondřej
%A Sido, Jakub
%A Zeman, Daniel
%A Zhu, Yilun
%Y Žabokrtský, Zdeněk
%Y Ogrodniczuk, Maciej
%S Proceedings of the CRAC 2022 Shared Task on Multilingual Coreference Resolution
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F zabokrtsky-etal-2022-findings
%X 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).
%U https://aclanthology.org/2022.crac-mcr.1
%P 1-17
Markdown (Informal)
[Findings of the Shared Task on Multilingual Coreference Resolution](https://aclanthology.org/2022.crac-mcr.1) (Žabokrtský et al., CRAC 2022)
ACL
- Zdeněk Žabokrtský, Miloslav Konopík, Anna Nedoluzhko, Michal Novák, Maciej Ogrodniczuk, Martin Popel, Ondřej Pražák, Jakub Sido, Daniel Zeman, and Yilun Zhu. 2022. Findings of the Shared Task on Multilingual Coreference Resolution. In Proceedings of the CRAC 2022 Shared Task on Multilingual Coreference Resolution, pages 1–17, Gyeongju, Republic of Korea. Association for Computational Linguistics.