@inproceedings{novak-etal-2026-findings,
    title = "Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities",
    author = "Nov{\'a}k, Michal  and
      Konop{\'i}k, Miloslav  and
      Nedoluzhko, Anna  and
      Popel, Martin  and
      Prazak, Ondrej  and
      Sido, Jakub  and
      Straka, Milan  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Zeman, Daniel",
    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://preview.aclanthology.org/ingest-acl-workshops/2026.codi-1.21/",
    pages = "130--161",
    ISBN = "979-8-89176-400-2",
    abstract = "This paper describes the fifth edition of the Shared Task on Multilingual Coreference Resolution, held in conjunction with the CODI-CRAC 2026 workshop. Building on previous iterations, the task required participants to develop systems capable of mention identification and identity-based coreference clustering. The 2026 edition specifically emphasizes long-range entities, defined as coreferential chains spanning significant distances, across many words and sentences. The task expanded its linguistic scope by incorporating five new datasets and two additional languages. These additions leverage version 1.4 of CorefUD, a harmonized multilingual collection comprising 27 datasets in 19 languages. In total, ten systems participated, including four LLM-based approaches (three fine-tuned models and one few-shot approach). While traditional systems still maintained their lead, LLMs demonstrated significant potential, suggesting they may soon challenge established approaches in future editions."
}