@inproceedings{akarajaradwong-chaksangchaichot-2025-cold,
    title = "Cold Starts and Hard Cases: A Two-Stage {SFT}-{RLVR} Approach for Legal Machine Translation (Just-{NLP} {L}-{MT} shared task)",
    author = "Akarajaradwong, Pawitsapak  and
      Chaksangchaichot, Chompakorn",
    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.9/",
    pages = "101--106",
    ISBN = "979-8-89176-312-8",
    abstract = "This paper details our system for the JUST-NLP 2025 Shared Task on English-to-Hindi Legal Machine Translation. We propose a novel two-stage, data-centric approach. First, we annotate the training data by translation difficulty and create easy and hard subsets.We perform SFT on the easier subset to establish a robust ``cold start''. Then, we apply RLVR exclusively on the harder subset, using machine translation metrics as reward signals. This strategy allowed our system to significantly outperform strong baselines, demonstrating the capability of our systems for machine translation tasks. Source code and model weights are available at https://github.com/ppaolong/FourCorners-JustNLP-MT-Shared-Task"
}