@inproceedings{pal-das-2025-checksent,
    title = "{C}heck{S}ent-{BN}: A {B}engali Multi-Task Dataset for Claim Checkworthiness and Sentiment Classification from News Headlines",
    author = "Pal, Pritam  and
      Das, Dipankar",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Hassan, Naeemul  and
      Prince, Enamul Hoque  and
      Tasnim, Mohiuddin  and
      Rony, Md Rashad Al Hasan  and
      Rahman, Md Tahmid Rahman",
    booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.banglalp-1.10/",
    pages = "119--130",
    ISBN = "979-8-89176-314-2",
    abstract = "This paper presents **CheckSent-BN** (Claim **Check**worthiness and **Sen**timent Classification in **B**engali **N**ews Headline), a novel multi-task dataset in Bengali comprising approximately 11.5K news headlines annotated for two critical natural language processing (NLP) tasks: claim checkworthiness detection and sentiment classification. To address the lack of high-quality annotated resources in Bengali, we employ a cost-effective annotation strategy that utilizes three large language models (GPT-4o-mini, GPT-4.1-mini, and Llama-4), followed by majority voting and manual verification to ensure label consistency. We provide benchmark results using multilingual and Bengali-focused transformer models under both single-task and multi-task learning (MTL) frameworks. Experimental results show that IndicBERTv2, BanglaBERT, and mDeBERTa model-based frameworks outperform other multilingual models, with IndicBERTv2 achieving the best overall performance in the MTL setting. CheckSent-BN establishes the first comprehensive benchmark for joint claim checkworthiness and sentiment classification in Bengali news headlines, offering a valuable resource for advancing misinformation detection and sentiment-aware analysis in low-resource languages. The CheckSent-BN dataset is available at: https://github.com/pritampal98/check-sent-bn"
}