@inproceedings{straka-2025-corpipe,
title = "{C}or{P}ipe at {CRAC} 2025: Evaluating Multilingual Encoders for Multilingual Coreference Resolution",
author = "Straka, Milan",
editor = "Ogrodniczuk, Maciej and
Novak, Michal and
Poesio, Massimo and
Pradhan, Sameer and
Ng, Vincent",
booktitle = "Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.crac-1.11/",
pages = "130--139",
abstract = "We present CorPipe 25, the winning entry to the CRAC 2025 Shared Task on Multilingual Coreference Resolution. This fourth iteration of the shared task introduces a new LLM track alongside the original unconstrained track, features reduced development and test sets to lower computational requirements, and includes additional datasets. CorPipe 25 represents a complete reimplementation of our previous systems, migrating from TensorFlow to PyTorch. Our system significantly outperforms all other submissions in both the LLM and unconstrained tracks by a substantial margin of 8 percentage points. The source code and trained models are publicly available at https://github.com/ufal/crac2025-corpipe."
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%0 Conference Proceedings
%T CorPipe at CRAC 2025: Evaluating Multilingual Encoders for Multilingual Coreference Resolution
%A Straka, Milan
%Y Ogrodniczuk, Maciej
%Y Novak, Michal
%Y Poesio, Massimo
%Y Pradhan, Sameer
%Y Ng, Vincent
%S Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%F straka-2025-corpipe
%X We present CorPipe 25, the winning entry to the CRAC 2025 Shared Task on Multilingual Coreference Resolution. This fourth iteration of the shared task introduces a new LLM track alongside the original unconstrained track, features reduced development and test sets to lower computational requirements, and includes additional datasets. CorPipe 25 represents a complete reimplementation of our previous systems, migrating from TensorFlow to PyTorch. Our system significantly outperforms all other submissions in both the LLM and unconstrained tracks by a substantial margin of 8 percentage points. The source code and trained models are publicly available at https://github.com/ufal/crac2025-corpipe.
%U https://aclanthology.org/2025.crac-1.11/
%P 130-139
Markdown (Informal)
[CorPipe at CRAC 2025: Evaluating Multilingual Encoders for Multilingual Coreference Resolution](https://aclanthology.org/2025.crac-1.11/) (Straka, CRAC 2025)
ACL