{
 "id": "ainlpml/IJCNLP-JustNLP-LMT",
 "owner": "ainlpml",
 "name": "IJCNLP-JustNLP-LMT",
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 "last_modified": "2026-01-19T14:42:31.000Z",
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 "citation": "@inproceedings{singh-etal-2025-findings,\n    title = \"Findings of the {JUST}-{NLP} 2025 Shared Task on {E}nglish-to-{H}indi Legal Machine Translation\",\n    author = \"Singh, Kshetrimayum Boynao  and\n      Kumar, Sandeep  and\n      Datta, Debtanu  and\n      Joshi, Abhinav  and\n      Mishra, Shivani  and\n      Paul, Shounak  and\n      Goyal, Pawan  and\n      Jain, Sarika  and\n      Ghosh, Saptarshi  and\n      Modi, Ashutosh  and\n      Ekbal, Asif\",\n    editor = \"Modi, Ashutosh  and\n      Ghosh, Saptarshi  and\n      Ekbal, Asif  and\n      Goyal, Pawan  and\n      Jain, Sarika  and\n      Joshi, Abhinav  and\n      Mishra, Shivani  and\n      Datta, Debtanu  and\n      Paul, Shounak  and\n      Singh, Kshetrimayum Boynao  and\n      Kumar, Sandeep\",\n    booktitle = \"Proceedings of the 1st Workshop on NLP for Empowering Justice (JUST-NLP 2025)\",\n    month = dec,\n    year = \"2025\",\n    address = \"Mumbai, India\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.justnlp-main.3/\",\n    pages = \"12--17\",\n    ISBN = \"979-8-89176-312-8\",\n    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 t",
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