@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Findings of the JUST-NLP 2025 Shared Task on English-to-Hindi Legal Machine Translation
%A Singh, Kshetrimayum Boynao
%A Kumar, Sandeep
%A Datta, Debtanu
%A Joshi, Abhinav
%A Mishra, Shivani
%A Paul, Shounak
%A Goyal, Pawan
%A Jain, Sarika
%A Ghosh, Saptarshi
%A Modi, Ashutosh
%A Ekbal, Asif
%Y Modi, Ashutosh
%Y Ghosh, Saptarshi
%Y Ekbal, Asif
%Y Goyal, Pawan
%Y Jain, Sarika
%Y Joshi, Abhinav
%Y Mishra, Shivani
%Y Datta, Debtanu
%Y Paul, Shounak
%Y Singh, Kshetrimayum Boynao
%Y Kumar, Sandeep
%S Proceedings of the 1st Workshop on NLP for Empowering Justice (JUST-NLP 2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-312-8
%F singh-etal-2025-findings
%X 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.
%U https://aclanthology.org/2025.justnlp-main.3/
%P 12-17
Markdown (Informal)
[Findings of the JUST-NLP 2025 Shared Task on English-to-Hindi Legal Machine Translation](https://aclanthology.org/2025.justnlp-main.3/) (Singh et al., JUSTNLP 2025)
ACL
- Kshetrimayum Boynao Singh, Sandeep Kumar, Debtanu Datta, Abhinav Joshi, Shivani Mishra, Shounak Paul, Pawan Goyal, Sarika Jain, Saptarshi Ghosh, Ashutosh Modi, and Asif Ekbal. 2025. Findings of the JUST-NLP 2025 Shared Task on English-to-Hindi Legal Machine Translation. In Proceedings of the 1st Workshop on NLP for Empowering Justice (JUST-NLP 2025), pages 12–17, Mumbai, India. Association for Computational Linguistics.