@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Musafir at BLP_2025 Task 2: Generating Python Code from Bangla Prompts using a Multi Model Cascade and Unit Test Validation
%A Hasan, Sakibul
%A Abdullah, Md Tasin
%A Al Mahmud, Abdullah
%A Banu, Ayesha
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Hassan, Naeemul
%Y Prince, Enamul Hoque
%Y Tasnim, Mohiuddin
%Y Rony, Md Rashad Al Hasan
%Y Rahman, Md Tahmid Rahman
%S Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-314-2
%F hasan-etal-2025-musafir
%X 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.
%U https://aclanthology.org/2025.banglalp-1.57/
%P 577-581
Markdown (Informal)
[Musafir at BLP_2025 Task 2: Generating Python Code from Bangla Prompts using a Multi Model Cascade and Unit Test Validation](https://aclanthology.org/2025.banglalp-1.57/) (Hasan et al., BanglaLP 2025)
ACL