@inproceedings{hasan-etal-2025-musafir,
    title = "Musafir at {BLP}{\_}2025 Task 2: Generating Python Code from {B}angla Prompts using a Multi Model Cascade and Unit Test Validation",
    author = "Hasan, Sakibul  and
      Abdullah, Md Tasin  and
      Al Mahmud, Abdullah  and
      Banu, Ayesha",
    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-1.57/",
    pages = "577--581",
    ISBN = "979-8-89176-314-2",
    abstract = "This paper presents our approach for the BLP25 Task 2: Code Generation in Bangla. To address the scarcity of Bangla{--}code training data, we adopt a two-stage pipeline. First, Bangla problem statements are translated into English using a neural translation model optimized for preserving technical semantics. Then, the translated text is passed to a Qwen-based code generation model to produce executable solutions. This translation{--}generation strategy leverages the strengths of English-centric code models while ensuring fidelity to the original Bangla instructions. Our system achieved competitive performance on the leaderboard, achieving the 3rd place with score of 91.8{\%} while demonstrating that translation-augmented pipelines are effective for low-resource code generation tasks."
}