@inproceedings{dhal-etal-2025-odiagenai,
    title = "{O}dia{G}en{AI} participation at {WAT} 2025",
    author = "Dhal, Debasish  and
      Sekhar, Sambit  and
      R, Revathy V  and
      Parida, Shantipriya  and
      Dhaka, Akash Kumar",
    editor = "Nakazawa, Toshiaki  and
      Goto, Isao",
    booktitle = "Proceedings of the Twelfth Workshop on Asian Translation (WAT 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.wat-1.11/",
    pages = "109--114",
    ISBN = "979-8-89176-309-8",
    abstract = "We at ODIAGEN, provide a detailed description of the model, training procedure, results and conclusion of our submission to the Workshop on Asian Translation (WAT 2025). For this year, we focus only on text to text translation tasks on low resource Indic languages targetting Hindi, Bengali, Malayalam and Odia languages specifically. The system uses a large language model NLLB-200 finetuned on large datasets consisting of over 100K rows for each targetted language. The whole training dataset is made of the data provided by the organisers as in previous years and augmented by a much larger 100K sentences of data subsampled from the Samanantar dataset provided by AI4Bharat. From a total of eight evaluation/challenge tests, our approach obtained the highest BLEU scores yet, since the conception on five."
}