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        <title>fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval</title>
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        <namePart type="given">Pranshu</namePart>
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    <abstract>The SemEval-2025 Task 7 on Multilingualand Crosslingual Fact-checked Claim Retrievalfocuses on retrieving relevant Fact-checkedclaims for social media Posts across multiplelanguages. This task is particularly challengingdue to linguistic barriers and the vast numberof languages Fact-checkers must consider.In this work, I approach the problem as aLearning-to-Rank task and solve it using abi-encoder-based model, fine-tuned on a pre-trained transformer optimized for sentence sim-ilarity. For the monolingual task, training wasperformed in both the source languages andtheir English translations. For cross-lingualretrieval, the training relied on English transla-tions.Most fine-tuned models have fewer than 500Mparameters, and the training was carried outefficiently using kaggle T4 GPUs with paral-lelization. Despite this lightweight setup, ourapproach achieved 92% Success@10 for mul-tilingual retrieval and 80% Success@10 forcross-lingual retrieval, securing 5th place inthe cross-lingual track and 10th place in themultilingual setting.</abstract>
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        <url>https://aclanthology.org/2025.semeval-1.306/</url>
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            <start>2352</start>
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%0 Conference Proceedings
%T fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval
%A Rastogi, Pranshu
%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 rastogi-2025-fact
%X The SemEval-2025 Task 7 on Multilingualand Crosslingual Fact-checked Claim Retrievalfocuses on retrieving relevant Fact-checkedclaims for social media Posts across multiplelanguages. This task is particularly challengingdue to linguistic barriers and the vast numberof languages Fact-checkers must consider.In this work, I approach the problem as aLearning-to-Rank task and solve it using abi-encoder-based model, fine-tuned on a pre-trained transformer optimized for sentence sim-ilarity. For the monolingual task, training wasperformed in both the source languages andtheir English translations. For cross-lingualretrieval, the training relied on English transla-tions.Most fine-tuned models have fewer than 500Mparameters, and the training was carried outefficiently using kaggle T4 GPUs with paral-lelization. Despite this lightweight setup, ourapproach achieved 92% Success@10 for mul-tilingual retrieval and 80% Success@10 forcross-lingual retrieval, securing 5th place inthe cross-lingual track and 10th place in themultilingual setting.
%U https://aclanthology.org/2025.semeval-1.306/
%P 2352-2357

Markdown (Informal)

[fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval](https://aclanthology.org/2025.semeval-1.306/) (Rastogi, SemEval 2025)

• fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval (Rastogi, SemEval 2025)

ACL

• Pranshu Rastogi. 2025. fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2352–2357, Vienna, Austria. Association for Computational Linguistics.