@inproceedings{peng-etal-2025-semeval,
title = "{S}em{E}val-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval",
author = "Peng, Qiwei and
Moro, Robert and
Gregor, Michal and
Srba, Ivan and
Ostermann, Simon and
Simko, Marian and
Podrouzek, Juraj and
Mesar{\v{c}}{\'i}k, Mat{\'u}{\v{s}} and
Kop{\v{c}}an, Jaroslav and
S{\o}gaard, Anders",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.323/",
pages = "2498--2511",
ISBN = "979-8-89176-273-2",
abstract = "The rapid spread of online disinformation presents a global challenge, and machine learning has been widely explored as a potential solution. However, multilingual settings and low-resource languages are often neglected in this field. To address this gap, we conducted a shared task on multilingual claim retrieval at SemEval 2025, aimed at identifying fact-checked claims that match newly encountered claims expressed in social media posts across different languages. The task includes two subtracks: 1) a monolingual track, where social posts and claims are in the same language 2) a crosslingual track, where social posts and claims might be in different languages. A total of 179 participants registered for the task contributing to 52 test submissions. 23 out of 31 teams have submitted their system papers. In this paper, we report the best-performing systems as well as the most common and the most effective approaches across both subtracks. This shared task, along with its dataset and participating systems, provides valuable insights into multilingual claim retrieval and automated fact-checking, supporting future research in this field."
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<abstract>The rapid spread of online disinformation presents a global challenge, and machine learning has been widely explored as a potential solution. However, multilingual settings and low-resource languages are often neglected in this field. To address this gap, we conducted a shared task on multilingual claim retrieval at SemEval 2025, aimed at identifying fact-checked claims that match newly encountered claims expressed in social media posts across different languages. The task includes two subtracks: 1) a monolingual track, where social posts and claims are in the same language 2) a crosslingual track, where social posts and claims might be in different languages. A total of 179 participants registered for the task contributing to 52 test submissions. 23 out of 31 teams have submitted their system papers. In this paper, we report the best-performing systems as well as the most common and the most effective approaches across both subtracks. This shared task, along with its dataset and participating systems, provides valuable insights into multilingual claim retrieval and automated fact-checking, supporting future research in this field.</abstract>
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%0 Conference Proceedings
%T SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
%A Peng, Qiwei
%A Moro, Robert
%A Gregor, Michal
%A Srba, Ivan
%A Ostermann, Simon
%A Simko, Marian
%A Podrouzek, Juraj
%A Mesarčík, Matúš
%A Kopčan, Jaroslav
%A Søgaard, Anders
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F peng-etal-2025-semeval
%X The rapid spread of online disinformation presents a global challenge, and machine learning has been widely explored as a potential solution. However, multilingual settings and low-resource languages are often neglected in this field. To address this gap, we conducted a shared task on multilingual claim retrieval at SemEval 2025, aimed at identifying fact-checked claims that match newly encountered claims expressed in social media posts across different languages. The task includes two subtracks: 1) a monolingual track, where social posts and claims are in the same language 2) a crosslingual track, where social posts and claims might be in different languages. A total of 179 participants registered for the task contributing to 52 test submissions. 23 out of 31 teams have submitted their system papers. In this paper, we report the best-performing systems as well as the most common and the most effective approaches across both subtracks. This shared task, along with its dataset and participating systems, provides valuable insights into multilingual claim retrieval and automated fact-checking, supporting future research in this field.
%U https://aclanthology.org/2025.semeval-1.323/
%P 2498-2511
Markdown (Informal)
[SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval](https://aclanthology.org/2025.semeval-1.323/) (Peng et al., SemEval 2025)
ACL
- Qiwei Peng, Robert Moro, Michal Gregor, Ivan Srba, Simon Ostermann, Marian Simko, Juraj Podrouzek, Matúš Mesarčík, Jaroslav Kopčan, and Anders Søgaard. 2025. SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2498–2511, Vienna, Austria. Association for Computational Linguistics.