@inproceedings{singh-etal-2025-adapting,
    title = "Adapting {I}ndic{T}rans2 for Legal Domain {MT} via {QL}o{RA} Fine-Tuning at {JUST}-{NLP} 2025",
    author = "Singh, Akoijam Jenil  and
      Meetei, Loitongbam Sanayai  and
      Surajkanta, Yumnam",
    editor = "Modi, Ashutosh  and
      Ghosh, Saptarshi  and
      Ekbal, Asif  and
      Goyal, Pawan  and
      Jain, Sarika  and
      Joshi, Abhinav  and
      Mishra, Shivani  and
      Datta, Debtanu  and
      Paul, Shounak  and
      Singh, Kshetrimayum Boynao  and
      Kumar, Sandeep",
    booktitle = "Proceedings of the 1st Workshop on NLP for Empowering Justice (JUST-NLP 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.justnlp-main.15/",
    pages = "142--147",
    ISBN = "979-8-89176-312-8",
    abstract = "Machine Translation (MT) in the legal domain presents substantial challenges due to its complex terminology, lengthy statutes, and rigid syntactic structures. The JUST-NLP 2025 Shared Task on Legal Machine Translation was organized to advance research on domain-specific MT systems for legal texts. In this work, we propose a fine-tuned version of the pretrained large language model (LLM) ai4bharat/indictrans2-en-indic-1B, a transformer-based English-to-Indic translation model. Fine-tuning was performed using the parallel corpus provided by the JUST-NLP 2025 Shared Task organizers.Our adapted model demonstrates notable improvements over the baseline system, particularly in handling domain-specific legal terminology and complex syntactic constructions. In automatic evaluation, our system obtained BLEU = 46.67 and chrF = 70.03.In human evaluation, it achieved adequacy = 4.085 and fluency = 4.006. Our approach achieved an AutoRank score of 58.79, highlighting the effectiveness of domain adaptation through fine-tuning for legal machine translation."
}