@inproceedings{singh-etal-2026-assamlegaltrans,
title = "{A}ssam{L}egal{T}rans: A Parallel Corpus, Benchmark and Analysis for {E}nglish-{A}ssamese Machine Translation of Legal Judgments",
author = "Singh, Telem Joyson and
Baruah, Hemanta and
Ranbir Singh, Sanasam and
Talukdar, Anindita and
Shahnaz, Nasrin and
Singh, Okram Jimmy and
Sarmah, Priyankoo and
Dutta, Pallav Kumar and
Nandi, Sukumar and
Duara, Pranab",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.386/",
doi = "10.63317/5q53i6shk3nm",
pages = "4921--4930",
abstract = "In India, the official language for writing judgments in higher courts is English, which creates a language barrier for citizens not proficient in English. Machine Translation (MT) provides a scalable solution, but its progress for low-resource languages like Assamese is significantly limited due to the lack of legal domain data. To address this gap, we introduce the first-of-its-kind English-Assamese parallel corpus for the translation of Indian court judgments. This dataset consists of over 55,000 manually translated and validated sentence pairs from over 500 judgments of the Gauhati High Court and the Supreme Court of India. Using this dataset, we perform a comprehensive evaluation of state-of-the-art multilingual models, including NLLB-200 and Sarvam-Translate, in both zero-shot and fine-tuned settings, comparing their performance against commercial systems. Our experiments show that fine-tuning on our legal-domain dataset significantly improves the translation quality. We also conduct a thorough error analysis that points out important issues in legal translation. These include precisely translating legal terms, properly transliterating named entities, expanding abbreviations, and transforming sentence structures, such as changing passive voice to active voice, when translating from English to Assamese. By creating a publicly available dataset and examining the specific challenges, this work offers a reproducible foundation and a clear way to develop more accurate and reliable legal machine translation systems. This will help improve access to justice for Assamese speakers."
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<abstract>In India, the official language for writing judgments in higher courts is English, which creates a language barrier for citizens not proficient in English. Machine Translation (MT) provides a scalable solution, but its progress for low-resource languages like Assamese is significantly limited due to the lack of legal domain data. To address this gap, we introduce the first-of-its-kind English-Assamese parallel corpus for the translation of Indian court judgments. This dataset consists of over 55,000 manually translated and validated sentence pairs from over 500 judgments of the Gauhati High Court and the Supreme Court of India. Using this dataset, we perform a comprehensive evaluation of state-of-the-art multilingual models, including NLLB-200 and Sarvam-Translate, in both zero-shot and fine-tuned settings, comparing their performance against commercial systems. Our experiments show that fine-tuning on our legal-domain dataset significantly improves the translation quality. We also conduct a thorough error analysis that points out important issues in legal translation. These include precisely translating legal terms, properly transliterating named entities, expanding abbreviations, and transforming sentence structures, such as changing passive voice to active voice, when translating from English to Assamese. By creating a publicly available dataset and examining the specific challenges, this work offers a reproducible foundation and a clear way to develop more accurate and reliable legal machine translation systems. This will help improve access to justice for Assamese speakers.</abstract>
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%0 Conference Proceedings
%T AssamLegalTrans: A Parallel Corpus, Benchmark and Analysis for English-Assamese Machine Translation of Legal Judgments
%A Singh, Telem Joyson
%A Baruah, Hemanta
%A Ranbir Singh, Sanasam
%A Talukdar, Anindita
%A Shahnaz, Nasrin
%A Singh, Okram Jimmy
%A Sarmah, Priyankoo
%A Dutta, Pallav Kumar
%A Nandi, Sukumar
%A Duara, Pranab
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F singh-etal-2026-assamlegaltrans
%X In India, the official language for writing judgments in higher courts is English, which creates a language barrier for citizens not proficient in English. Machine Translation (MT) provides a scalable solution, but its progress for low-resource languages like Assamese is significantly limited due to the lack of legal domain data. To address this gap, we introduce the first-of-its-kind English-Assamese parallel corpus for the translation of Indian court judgments. This dataset consists of over 55,000 manually translated and validated sentence pairs from over 500 judgments of the Gauhati High Court and the Supreme Court of India. Using this dataset, we perform a comprehensive evaluation of state-of-the-art multilingual models, including NLLB-200 and Sarvam-Translate, in both zero-shot and fine-tuned settings, comparing their performance against commercial systems. Our experiments show that fine-tuning on our legal-domain dataset significantly improves the translation quality. We also conduct a thorough error analysis that points out important issues in legal translation. These include precisely translating legal terms, properly transliterating named entities, expanding abbreviations, and transforming sentence structures, such as changing passive voice to active voice, when translating from English to Assamese. By creating a publicly available dataset and examining the specific challenges, this work offers a reproducible foundation and a clear way to develop more accurate and reliable legal machine translation systems. This will help improve access to justice for Assamese speakers.
%R 10.63317/5q53i6shk3nm
%U https://aclanthology.org/2026.lrec-1.386/
%U https://doi.org/10.63317/5q53i6shk3nm
%P 4921-4930
Markdown (Informal)
[AssamLegalTrans: A Parallel Corpus, Benchmark and Analysis for English-Assamese Machine Translation of Legal Judgments](https://aclanthology.org/2026.lrec-1.386/) (Singh et al., LREC 2026)
ACL
- Telem Joyson Singh, Hemanta Baruah, Sanasam Ranbir Singh, Anindita Talukdar, Nasrin Shahnaz, Okram Jimmy Singh, Priyankoo Sarmah, Pallav Kumar Dutta, Sukumar Nandi, and Pranab Duara. 2026. AssamLegalTrans: A Parallel Corpus, Benchmark and Analysis for English-Assamese Machine Translation of Legal Judgments. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 4921–4930, Palma de Mallorca, Spain. ELRA Language Resource Association.