@inproceedings{singh-etal-2025-findings,
    title = "Findings of the {JUST}-{NLP} 2025 Shared Task on {E}nglish-to-{H}indi Legal Machine Translation",
    author = "Singh, Kshetrimayum Boynao  and
      Kumar, Sandeep  and
      Datta, Debtanu  and
      Joshi, Abhinav  and
      Mishra, Shivani  and
      Paul, Shounak  and
      Goyal, Pawan  and
      Jain, Sarika  and
      Ghosh, Saptarshi  and
      Modi, Ashutosh  and
      Ekbal, Asif",
    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.3/",
    pages = "12--17",
    ISBN = "979-8-89176-312-8",
    abstract = "This paper provides an overview of the Shared Task on Legal Machine Translation (L-MT), organized as part of the JUST-NLP 2025 Workshop at IJCNLP-AACL 2025, aimed at improving the translation of legal texts, a domain where precision, structural faithfulness, and terminology preservation are essential. The training set comprises 50,000 sentences, with 5,000 sentences each for the validation and test sets. The submissions employed strategies such as: domain-adaptive fine-tuning of multilingual models, QLoRA-based parameter-efficient adaptation, curriculum-guided supervised training, reinforcement learning with verifiable MT metrics, and from-scratch Transformer training. The systems are evaluated based on BLEU, METEOR, TER, chrF++, BERTScore, and COMET metrics. We also combine the scores of these metrics to give an average score (AutoRank). The top-performing system is based on a fine-tuned distilled NLLB-200 model and achieved the highest AutoRank score of 72.1. Domain adaptation consistently yielded substantial improvements over baseline models, and precision-focused rewards proved especially effective for the legal MT. The findings also highlight that large multilingual Transformers can deliver accurate and reliable English-to-Hindi legal translations when carefully fine-tuned on legal data, advancing the broader goal of improving access to justice in multilingual settings."
}