@inproceedings{acharya-etal-2025-ju,
    title = "{JU}-{NLP}: Improving Low-Resource {I}ndic Translation System with Efficient {L}o{RA}-Based Adaptation",
    author = "Acharya, Priyobroto  and
      Mondal, Haranath  and
      Saha, Dipanjan  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.wmt-1.95/",
    doi = "10.18653/v1/2025.wmt-1.95",
    pages = "1201--1209",
    ISBN = "979-8-89176-341-8",
    abstract = "Low-resource Indic languages such as Assamese, Manipuri, Mizo, and Bodo face persistent challenges in NMT due to limited parallel data, diverse scripts, and complex morphology. We address these issues in the WMT {\$}2025{\$} shared task by introducing a unified multilingual NMT framework that combines rigorous language-specific preprocessing with parameter-efficient adaptation of large-scale models. Our pipeline integrates the NLLB-{\$}200{\$} and IndicTrans{\$}2{\$} architectures, fine-tuned using LoRA and DoRA, reducing trainable parameters by over 90{\%} without degrading translation quality. A comprehensive preprocessing suite, including Unicode normalization, semantic filtering, transliteration, and noise reduction, ensures high-quality inputs, while script-aware post-processing mitigates evaluation bias from orthographic mismatches. Experiments across English-Indic directions demonstrate that NLLB-{\$}200{\$} achieves superior results for Assamese, Manipuri, and Mizo, whereas IndicTrans{\$}2{\$} excels in English-Bodo. Evaluated using BLEU, chrF, METEOR, ROUGE-L, and TER, our approach yields consistent improvements over baselines, underscoring the effectiveness of combining efficient fine-tuning with linguistically informed preprocessing for low-resource Indic MT."
}