CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems
Authors:
Soham Bhattacharjee,
Mukund K Roy,
Yathish Poojary,
Bhargav Dave,
Mihir Raj,
Vandan Mujadia,
Baban Gain,
Pruthwik Mishra,
Arafat Ahsan,
Parameswari Krishnamurthy,
Ashwath Rao,
Gurpreet Singh Josan,
Preeti Dubey,
Aadil Amin Kak,
Anna Rao Kulkarni,
Narendra VG,
Sunita Arora,
Rakesh Balbantray,
Prasenjit Majumdar,
Karunesh K Arora,
Asif Ekbal,
Dipti Mishra Sharma
Abstract:
India's linguistic landscape is one of the most diverse in the world, comprising over 120 major languages and approximately 1,600 additional languages, with 22 officially recognized as scheduled languages in the Indian Constitution. Despite recent progress in multilingual neural machine translation (NMT), high-quality parallel corpora for Indian languages remain scarce, especially across varied do…
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India's linguistic landscape is one of the most diverse in the world, comprising over 120 major languages and approximately 1,600 additional languages, with 22 officially recognized as scheduled languages in the Indian Constitution. Despite recent progress in multilingual neural machine translation (NMT), high-quality parallel corpora for Indian languages remain scarce, especially across varied domains. In this paper, we introduce a large-scale, high-quality annotated parallel corpus covering 11 of these languages : English, Telugu, Hindi, Punjabi, Odia, Kashmiri, Sindhi, Dogri, Kannada, Urdu, and Gujarati comprising a total of 772,000 bi-text sentence pairs. The dataset is carefully curated and systematically categorized into three key domains: Government, Health, and General, to enable domain-aware machine translation research and facilitate effective domain adaptation. To demonstrate the utility of CorIL and establish strong benchmarks for future research, we fine-tune and evaluate several state-of-the-art NMT models, including IndicTrans2, NLLB, and BhashaVerse. Our analysis reveals important performance trends and highlights the corpus's value in probing model capabilities. For instance, the results show distinct performance patterns based on language script, with massively multilingual models showing an advantage on Perso-Arabic scripts (Urdu, Sindhi) while other models excel on Indic scripts. This paper provides a detailed domain-wise performance analysis, offering insights into domain sensitivity and cross-script transfer learning. By publicly releasing CorIL, we aim to significantly improve the availability of high-quality training data for Indian languages and provide a valuable resource for the machine translation research community.
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Submitted 24 September, 2025;
originally announced September 2025.