@inproceedings{panchendrarajan-etal-2025-claimcatchers,
    title = "{C}laim{C}atchers at {S}em{E}val-2025 Task 7: Sentence Transformers for Claim Retrieval",
    author = "Panchendrarajan, Rrubaa  and
      Frade, Rafael  and
      Zubiaga, Arkaitz",
    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-1.63/",
    pages = "455--462",
    ISBN = "979-8-89176-273-2",
    abstract = "Retrieving previously fact-checked claims from verified databases has become a crucial area of research in automated fact-checking, given the impracticality of manual verification of massive online content. To address this challenge, SemEval 2025 Task 7 focuses on multilingual previously fact-checked claim retrieval. This paper presents the experiments conducted for this task, evaluating the effectiveness of various sentence transformer models{---}ranging from 22M to 9B parameters{---}in conjunction with retrieval strategies such as nearest neighbor search and reranking techniques. Further, we explore the impact of learning context-specific text representation via finetuning these models. Our results demonstrate that smaller and medium-sized models, when optimized with effective finetuning and reranking, can achieve retrieval accuracy comparable to larger models, highlighting their potential for scalable and efficient misinformation detection."
}