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\title{Findings of the Fifth Shared Task on Multilingual Coreference Resolution: 
Expanding Datasets for Long-Range Entities}
%Expanding Datasets for Long-Context Resolution\\
%Processing Long Documents in the Generative Era\\
%Assessing Neural and Generative Approaches on Long Documents


% Author information can be set in various styles:
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% To start a seperate ``row'' of authors use \AND, as in
% \author{Author 1 \\ Address line \\  ... \\ Address line
%         \AND
%         Author 2 \\ Address line \\ ... \\ Address line \And
%         Author 3 \\ Address line \\ ... \\ Address line}

\author{
Michal Novák$^1$,
Miloslav Konopík$^2$,
Anna Nedoluzhko$^1$,
Martin Popel$^1$, \\
\textbf{Ondřej Pražák$^2$,
Jakub Sido$^2$,
Milan Straka$^1$,
Zdeněk Žabokrtský$^1$,
Daniel Zeman$^1$} \\[2mm]
$^1$ Charles University, Faculty of Mathematics and Physics, \\ Institute of Formal and Applied Linguistics, Prague, Czechia \\
\texttt{\{mnovak,nedoluzko,popel,straka,zabokrtsky,zeman\}@ufal.mff.cuni.cz}\\[2mm]
$^2$
University of West Bohemia, Faculty of Applied Sciences, \\ Department of Computer Science and Engineering, Pilsen, Czechia \\
\texttt{\{konopik,ondfa,sidoj\}@kiv.zcu.cz}\\[2mm]
}


%\author{
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%  Affiliation / Address line 3 \\
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%  Second Author \\
%  Affiliation / Address line 1 \\
%  Affiliation / Address line 2 \\
%  Affiliation / Address line 3 \\
%  \texttt{email@domain} \\}


\usepackage{booktabs}
%\usepackage{multicol}

% Počty korpusů a jazyků v CRAC24, tedy ve veřejné části CorefUDu 1.3, ale bez těch malých (ParCorFull)
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\newcommand{\nsystems}[0]{ten} % Bez baseline a baselineGZ a bez Bruno Gatti, který nechce být nikde uveden, 4 LLM + 6 UNC
\newcommand{\nteams}[0]{eight} % ÚFAL CorPipe poslal 3 systémy, zbylých 7 týmů jen 1 systém

\hyphenation{CorefUD}

\begin{document}
\maketitle

\begin{abstract}

% The paper presents an overview of the fifth edition of the Shared Task on
% Multilingual Coreference Resolution, organized as part of the CODI-CRAC 2026 workshop. 
% As in the previous editions, participants were challenged to develop systems that identify mentions and cluster them according to identity coreference.
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 focus of this year's task is long-range entities,
% i.e., coreference entities where the last mention is many words after the first mention, spanning many sentences in long documents.
The 2026 edition specifically emphasizes long-range entities, defined as coreferential chains spanning significant distances, across many words and sentences.

% The task also expanded its coverage with five new datasets and two additional languages, using version 1.4 of CorefUD -- a harmonized multilingual collection of \ndatasets{} datasets in \nlanguages{} languages.
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 \ndatasets{} datasets in \nlanguages{} languages.

%(two fine-tuned and two using few-shot adaptation).
%\todo{Někdo tu napsal two fine-tuned, změnil jsem na three.}
% Martin: LLM-UWB i LLM-LatticeNLP jsou určitě fine-tuned. LLM-Landcore je určitě few-shot.
% LLM-PortNLP je určitě taky fine-tuned. V dotazníku sice vyplnil i "in-context learning",
% ale v popisu "Training data included language-specific few-shot examples (3 per language)"
% a z toho bych soudil spíš, že ty few=3 examples=documents per language byly použity ve fázi
% fine-tuning=tréningu, tedy se vůbec nejedná o in-context learning.
% Je sice možné, že použil i fine-tuning i skutečný in-context learning,
% ale stejně bych to neoznačoval jako few-shot přístup.
In total, \nsystems{} 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.
\end{abstract}

\input{01-intro}
\input{02-data}
\input{03-metrics}
\input{04-systems}
\input{05-results}
\input{06-concl}



\section*{Acknowledgements}

\forcamera{update}
This work has been supported
% Mišo, Zdeněk
by Charles University Research Centre program No. 24/SSH/009,
% Dan a Mišo
Ministry of Education, Youth, and Sports of the Czech Republic, Project No.
LM2023062 LINDAT/CLARIAH-CZ and
% Martin, Milan OP JAK Mezisektor (https://ufal.mff.cuni.cz/node/2810)
CZ.02.01.01/00/23\_020/0008518,
% ZČU
and by the project R\&D of Technologies for Advanced Digitalization in the Pilsen Metropolitan Area (DigiTech) No. CZ.02.01.01/00/23\_021/0008436.
% Dan
%and the Grant 20-16819X (LUSyD) of the Czech Science Foundation (GAČR).
We thank all the participants of the shared task for participating and for
providing brief descriptions of their systems.
We also thank anonymous reviewers for their useful remarks.

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\input{07-appendix}

\end{document}
