@inproceedings{joshi-etal-2025-bvslp,
    title = "{BVSLP}: Machine Translation Using Linguistic Embellishments for {I}ndic{MT} Shared Task 2025",
    author = "Joshi, Nisheeth  and
      Arora, Palak  and
      Krishnia, Anju  and
      Lonchenpa, Riya  and
      Vizo, Mhasilenuo",
    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/ingest-emnlp/2025.wmt-1.105/",
    pages = "1265--1270",
    ISBN = "979-8-89176-341-8",
    abstract = "This paper describes our submission to the Indic MT 2025 shared task, where we trained machine translation systems for five low-resource language pairs: English{--}Manipuri, Manipuri{--}English, English{--}Bodo, English{--}Assamese, and Assamese{--}English. To address the challenge of out-of-vocabulary errors, we introduced a Named Entity Translation module that automatically identified named entities and either translated or transliterated them into the target language. The augmented corpus produced by this module was used to fine-tune a Transformer-based neural machine translation system. Our approach, termed HEMANT (Highly Efficient Machine-Assisted Natural Translation), demonstrated consistent improvements, particularly in reducing named entity errors and improving fluency for Assamese{--}English and Manipuri{--}English. Official shared task evaluation results show that the system achieved competitive performance across all five language pairs, underscoring the effectiveness of linguistically informed preprocessing for low-resource Indic MT."
}