@inproceedings{shanto-etal-2025-mdc3,
    title = "{MDC}$^3$: A Novel Multimodal Dataset for Commercial Content Classification in {B}engali",
    author = "Shanto, Anik Mahmud  and
      Priya, Mst. Sanjida Jamal  and
      Tamim, Fahim Shakil  and
      Hoque, Mohammed Moshiul",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-srw.31/",
    pages = "311--320",
    ISBN = "979-8-89176-192-6",
    abstract = "Identifying commercial posts in resource-constrained languages among diverse and unstructured content remains a significant challenge for automatic text classification tasks. To address this, this work introduces a novel dataset named MDC$^3$ (Multimodal Dataset for Commercial Content Classification), comprising 5,007 annotated Bengali social media posts classified as commercial and noncommercial. A comprehensive annotation guideline accompanying the dataset is included to aid future dataset creation in resource-constrained languages. Furthermore, we performed extensive experiments on MDC$^3$ considering both unimodal and multimodal domains. Specifically, the late fusion of textual (mBERT) and visual (ViT) models (i.e., ViT+mBERT) achieves the highest F1 score of 90.91, significantly surpassing other baselines."
}