@inproceedings{jagadeeshan-etal-2026-chandomitra,
    title = "Chandomitra: Towards Generating Structured {S}anskrit Poetry from Natural Language Inputs",
    author = "Jagadeeshan, Manoj Balaji  and
      Bhatia, Samarth  and
      Ray, Pretam  and
      Surana, Harshul Raj  and
      P, Akhil Rajeev  and
      Mishra, Priya  and
      Kulkarni, Annarao  and
      Ramakrishnan, Ganesh  and
      Ap, Prathosh  and
      Goyal, Pawan",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.eacl-long.24/",
    pages = "518--534",
    ISBN = "979-8-89176-380-7",
    abstract = "Text Generation has achieved remarkable performance using large language models. It has also been recently well-studied that these large language models are capable of creative generation tasks but prominently for high-resource languages. This prompts a fundamental question: $\textit{Is there a way to utilize these (large) language models for structured poetry generation in a low-resource language, such as Sanskrit?}$ We present Chandomitra, an English input to structured Sanskrit Poetry translation dataset, specifically adhering to the Anushtubh meter. We benchmark various open and closed models, and scrutinize specialized techniques such as constrained decoding and instruction fine-tuning, for the proposed task. Our constrained decoding methodology achieves 99.86{\%} syntactic accuracy in generating metrically valid Sanskrit poetry, outperforming GPT-4o (1-shot: 31.24{\%}). Our best-performing instruction-tuned model, on the other hand, performs better in semantic coherence with the English input, at the expense of slightly lower syntactic accuracy. Human evaluation further reveals that instruction fine-tuned model is better able to capture the poetic aspects."
}