@inproceedings{roy-etal-2025-enhancing,
    title = "Enhancing Textual Understanding: Automated Claim Span Identification in {E}nglish, {H}indi, {B}engali, and {C}ode{M}ix",
    author = "Roy, Rudra  and
      Pal, Pritam  and
      Das, Dipankar  and
      Ghosh, Saptarshi  and
      Paul, Biswajit",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.118/",
    pages = "1030--1035",
    abstract = "Claim span identification, a crucial task in Natural Language Processing (NLP), aims to extract specific claims from texts. Such claim spans can be further utilized in various critical NLP applications, such as claim verification, fact-checking, and opinion mining, among others. The present work proposes a multilingual claim span identification framework for handling social media data in English, Hindi, Bengali, and CodeMixed texts, leveraging the strengths and knowledge of transformer-based pre-trained models. Our proposed framework efficiently identifies the contextual relationships between words and precisely detects claim spans across all languages, achieving a high F1 score and Jaccard score. The source code and datasets are available at: https://github.com/pritampal98/claim-span-multilingual"
}