@inproceedings{nayak-etal-2025-ju,
    title = "{JU}{\_}{NLP} at {S}em{E}val-2025 Task 7: Leveraging Transformer-Based Models for Multilingual {\&} Crosslingual Fact-Checked Claim Retrieval",
    author = "Nayak, Atanu  and
      Debnath, Srijani  and
      Majumdar, Arpan  and
      Pal, Pritam  and
      Das, Dipankar",
    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-1.271/",
    pages = "2084--2089",
    ISBN = "979-8-89176-273-2",
    abstract = "Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper presents a systematic approach for the retrieval of top-k relevant fact-checks for a given post in a monolingual and cross-lingual setup using transformer-based pre-trained models fine-tuned with a dual encoder architecture. By training and evaluating the shared task test dataset, our proposed best-performing framework achieved an average success@10 score of 0.79 and 0.62 for the retrieval of 10 fact-checks from the fact-check corpus against a post in monolingual and crosslingual track respectively."
}