@inproceedings{amina-etal-2025-form,
title = "Form-aware Poetic Generation for {B}angla",
author = "Amina and
Abdullah and
Al Mushabbir, Mueeze and
Ahmed, Sabbir",
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-1.30/",
pages = "366--372",
ISBN = "979-8-89176-314-2",
abstract = "Poetry generation in low-resource languages such as Bangla is particularly challenging due to the scarcity of structured poetic corpora and the complexity of its metrical system (matra). We present a structure-aware framework for Bangla poetry generation using pretrained Bangla large language models (LLMs){--}TigerLLM, TituLLM, and BanglaT5{--}trained on general non-poetic text corpora augmented with rich structural control tokens. These tokens capture rhyme, meter, word count, and line boundaries, enabling unsupervised modeling of poetic form without curated poetry datasets. Unlike prior fixed-pattern approaches, our framework introduces variable control compositions, allowing models to generate flexible poetic structures. Experiments show that explicit structural conditioning improves rhyme consistency and metrical balance while maintaining semantic coherence. Our study provides the first systematic evaluation of Bangla LLMs for form-constrained creative generation, offering insights into structural representation in low-resource poetic modeling."
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<abstract>Poetry generation in low-resource languages such as Bangla is particularly challenging due to the scarcity of structured poetic corpora and the complexity of its metrical system (matra). We present a structure-aware framework for Bangla poetry generation using pretrained Bangla large language models (LLMs)–TigerLLM, TituLLM, and BanglaT5–trained on general non-poetic text corpora augmented with rich structural control tokens. These tokens capture rhyme, meter, word count, and line boundaries, enabling unsupervised modeling of poetic form without curated poetry datasets. Unlike prior fixed-pattern approaches, our framework introduces variable control compositions, allowing models to generate flexible poetic structures. Experiments show that explicit structural conditioning improves rhyme consistency and metrical balance while maintaining semantic coherence. Our study provides the first systematic evaluation of Bangla LLMs for form-constrained creative generation, offering insights into structural representation in low-resource poetic modeling.</abstract>
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%0 Conference Proceedings
%T Form-aware Poetic Generation for Bangla
%A Al Mushabbir, Mueeze
%A Ahmed, Sabbir
%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
%A Amina
%A Abdullah
%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 amina-etal-2025-form
%X Poetry generation in low-resource languages such as Bangla is particularly challenging due to the scarcity of structured poetic corpora and the complexity of its metrical system (matra). We present a structure-aware framework for Bangla poetry generation using pretrained Bangla large language models (LLMs)–TigerLLM, TituLLM, and BanglaT5–trained on general non-poetic text corpora augmented with rich structural control tokens. These tokens capture rhyme, meter, word count, and line boundaries, enabling unsupervised modeling of poetic form without curated poetry datasets. Unlike prior fixed-pattern approaches, our framework introduces variable control compositions, allowing models to generate flexible poetic structures. Experiments show that explicit structural conditioning improves rhyme consistency and metrical balance while maintaining semantic coherence. Our study provides the first systematic evaluation of Bangla LLMs for form-constrained creative generation, offering insights into structural representation in low-resource poetic modeling.
%U https://aclanthology.org/2025.banglalp-1.30/
%P 366-372
Markdown (Informal)
[Form-aware Poetic Generation for Bangla](https://aclanthology.org/2025.banglalp-1.30/) (Amina et al., BanglaLP 2025)
ACL
- Amina, Abdullah, Mueeze Al Mushabbir, and Sabbir Ahmed. 2025. Form-aware Poetic Generation for Bangla. In Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025), pages 366–372, Mumbai, India. Association for Computational Linguistics.