@inproceedings{bhuiyan-etal-2025-bhasabodh,
    title = "{B}hasa{B}odh: Bridging {B}angla Dialects and {R}omanized Forms through Machine Translation",
    author = "Bhuiyan, Md. Tofael Ahmed  and
      Rahman, Md. Abdur  and
      Masum, Abdul Kadar Muhammad",
    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-1.9/",
    pages = "113--118",
    ISBN = "979-8-89176-314-2",
    abstract = "While machine translation has made significant strides for high-resource languages, many regional languages and their dialects, such as the Bangla variants Chittagong and Sylhet, remain underserved. Existing resources are often insufficient for robust sentence-level evaluation and overlook the widespread real-world practice of romanization, the common practice of typing native languages using the Latin script in digital communication. To address these gaps, we introduce BhasaBodh, a comprehensive benchmark for Bangla dialectal machine translation. We construct and release a sentence-level parallel dataset for Chittagong and Sylhet dialects aligned with Standard Bangla and English, create a novel romanized version of the dialectal data to facilitate evaluation in realistic multi-script scenarios, and provide the first comprehensive performance baselines by fine-tuning two powerful multilingual models, NLLB-200 and mBART-50, on seven distinct translation tasks. Our experiments reveal that mBART-50 consistently outperforms NLLB-200 on most dialectal and romanized tasks, achieving a BLEU score as high as 87.44 on the Romanized-to-Standard Bangla normalization task. However, complex cross-lingual and cross-script translation remains a significant challenge. BhasaBodh lays the groundwork for future research in low-resource dialectal NLP, offering a valuable resource for developing more inclusive and practical translation systems."
}