@inproceedings{rastogi-2025-fact,
    title = "fact check {AI} at {S}em{E}val-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval",
    author = "Rastogi, Pranshu",
    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://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.306/",
    pages = "2352--2357",
    ISBN = "979-8-89176-273-2",
    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."
}