Proceedings of the CRAC 2022 Shared Task on Multilingual Coreference Resolution
Zdeněk Žabokrtský, Maciej Ogrodniczuk (Editors)
- Anthology ID:
- 2022.crac-mcr
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- CRAC
- SIG:
- Publisher:
- Association for Computational Linguistics
- URL:
- https://aclanthology.org/2022.crac-mcr
- DOI:
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2022.crac-mcr.pdf
Proceedings of the CRAC 2022 Shared Task on Multilingual Coreference Resolution
Zdeněk Žabokrtský
|
Maciej Ogrodniczuk
Findings of the Shared Task on Multilingual Coreference Resolution
Zdeněk Žabokrtský
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Miloslav Konopík
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Anna Nedoluzhko
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Michal Novák
|
Maciej Ogrodniczuk
|
Martin Popel
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Ondřej Pražák
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Jakub Sido
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Daniel Zeman
|
Yilun Zhu
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).
Coreference Resolution for Polish: Improvements within the CRAC 2022 Shared Task
Karol Saputa
The paper presents our system for coreference resolution in Polish. We compare the system with previous works for the Polish language as well as with the multilingual approach in the CRAC 2022 Shared Task on Multilingual Coreference Resolution thanks to a universal, multilingual data format and evaluation tool. We discuss the accuracy, computational performance, and evaluation approach of the new System which is a faster, end-to-end solution.
End-to-end Multilingual Coreference Resolution with Mention Head Prediction
Ondřej Pražák
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Miloslav Konopik
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.
ÚFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution
Milan Straka
|
Jana Straková
We describe the winning submission to the CRAC 2022 Shared Task on Multilingual Coreference Resolution. Our system first solves mention detection and then coreference linking on the retrieved spans with an antecedent-maximization approach, and both tasks are fine-tuned jointly with shared Transformer weights. We report results of finetuning a wide range of pretrained models. The center of this contribution are fine-tuned multilingual models. We found one large multilingual model with sufficiently large encoder to increase performance on all datasets across the board, with the benefit not limited only to the underrepresented languages or groups of typologically relative languages. The source code is available at https://github.com/ufal/crac2022-corpipe.