@inproceedings{bourgois-etal-2026-closing,
title = "Closing the Gap at {CRAC} 2026: Two-Stage Adaptation for {LLM}-Based Multilingual Coreference Resolution",
author = "Bourgois, Antoine and
Seminck, Olga and
Poibeau, Thierry",
editor = "Braud, Chlo{\'e} and
Hardmeier, Christian and
Ogrodniczuk, Maciej and
Loaiciga, Sharid and
Zeldes, Amir and
Nov{\'a}k, Michal and
Li, Chuyuan and
Strube, Michael and
Li, Junyi Jessy",
booktitle = "Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference ({CODI}-{CRAC} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.codi-1.23/",
pages = "167--183",
ISBN = "979-8-89176-400-2",
abstract = "We present our submission to the LLM track of the 2026 Computational Models of Reference, Anaphora and Coreference (CRAC 2026) shared task. With an average CoNLL F1 score of 74.32 on the official test set, our system ranked first in the LLM track, and third overall. Our system is based on the Gemma-3-27b model, fine-tuned using a two-stage strategy with a multilingual base adapter followed by dataset-specific adapters. We represent mention spans by their headword using an XML-inspired format with local reindexing and annotate documents iteratively. These design choices proved effective across languages, document lengths, and annotation guidelines."
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%0 Conference Proceedings
%T Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution
%A Bourgois, Antoine
%A Seminck, Olga
%A Poibeau, Thierry
%Y Braud, Chloé
%Y Hardmeier, Christian
%Y Ogrodniczuk, Maciej
%Y Loaiciga, Sharid
%Y Zeldes, Amir
%Y Novák, Michal
%Y Li, Chuyuan
%Y Strube, Michael
%Y Li, Junyi Jessy
%S Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference (CODI-CRAC 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-400-2
%F bourgois-etal-2026-closing
%X We present our submission to the LLM track of the 2026 Computational Models of Reference, Anaphora and Coreference (CRAC 2026) shared task. With an average CoNLL F1 score of 74.32 on the official test set, our system ranked first in the LLM track, and third overall. Our system is based on the Gemma-3-27b model, fine-tuned using a two-stage strategy with a multilingual base adapter followed by dataset-specific adapters. We represent mention spans by their headword using an XML-inspired format with local reindexing and annotate documents iteratively. These design choices proved effective across languages, document lengths, and annotation guidelines.
%U https://aclanthology.org/2026.codi-1.23/
%P 167-183
Markdown (Informal)
[Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution](https://aclanthology.org/2026.codi-1.23/) (Bourgois et al., CODI-CRAC 2026)
ACL