README.md

metadata

license: cc-by-4.0
language:
  - en
  - hi
  - gu
  - ks
  - te
  - kn
  - pa
  - or
  - ur
  - sd
  - doi

Indic Parallel Corpus: 11 Indian Language Pairs for Machine Translation

This repository contains a parallel corpus for machine translation across 11 Indian language pairs. The data is curated to cover three distinct domains: Governance, Health, and General. This dataset is designed to help researchers and developers build and evaluate robust machine translation models for Indian languages.

Dataset Description

The corpus provides parallel sentences for a variety of language pairs, with a focus on Hindi as a pivot language. All translation pairs are bidirectional. The data has been sourced and cleaned to be useful for training Neural Machine Translation (NMT) models.

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Languages Covered

The dataset includes the following 11 language pairs:

Source Language	Target Language	Language Codes
Hindi	Gujarati	hi - gu
Hindi	Kashmiri	hi - ks
Hindi	Telugu	hi - te
Hindi	Kannada	hi - kn
Hindi	Punjabi	hi - pa
Hindi	Oriya	hi - or
Hindi	Urdu	hi - ur
Hindi	Sindhi	hi - sd
Hindi	Dogri	hi - doi
English	Hindi	en - hi
Telugu	English	te - en

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Dataset Structure and Statistics

The data is organized by language pair and domain. Each language pair directory contains sub-directories for the specific domains. The following table provides a detailed breakdown of the number of parallel sentences for each language pair, domain, and data split (train/dev/test). An empty cell indicates that data for that specific domain is not available.

Language Pair	General (Train)	General (Dev)	General (Test)	Governance (Train)	Governance (Dev)	Governance (Test)	Health (Train)	Health (Dev)	Health (Test)
dg_hi	12,411	500	500	6,947	500	500
en_hi				38,790	500	500	10,043	500	500
en_te				9,976	500	500	17,237	500	500
gu_hi				18,850	500	500
hi_dg				30,359	500	500	4,343	500	500
hi_en				42,964	500	500	12,187	500	500
hi_gu				26,335	500	500	4,899	500	500
hi_kn				27,531	500	500	16,351	500	500
hi_ks	21,103	500	500
hi_or				24,291	500	500	9,387	500	500
hi_pa				30,373	500	500	11,328	500	500
hi_sd	21,548	500	500				13,233	500	500
hi_te				8,061	500	500	11,911	500	500
hi_ur	8,956	500	500	9,929	500	500	5,271	500	500
kn_hi				16,040	500	500	19,148	500	500
ks_ur	2,606	500	500
or_hi				11,581	500	500	18,308	500	500
pa_hi				22,098	500	500	22,532	500	500
sd_hi	3,499	500	500
te_en				5,527	500	500	6,008	500	500
te_hi				4,405	500	500	18,246	500	500
ur_hi	27,791	500	500	8,938	500	500	6,259	500	500
ur_ks	22,820	500	500

Domains

1. Governance: Includes sentences from government documents, press releases, and legal texts.
2. Health: Comprises text from medical journals, healthcare advisories, and public health communications.
3. General: A broad category including sentences from news articles, websites, and miscellaneous sources.

Data Format

Each dataset configuration is provided as a single tab-separated text file (.txt).

Each line in the file represents a parallel sentence pair, with the source language sentence and the target language sentence separated by a single tab character (\t).

Citation

If you use this dataset in your research, please consider citing it.

@misc{bhattacharjee2025corilenrichingindianlanguage,
      title={CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems}, 
      author={Soham Bhattacharjee and Mukund K Roy and Yathish Poojary and Bhargav Dave and Mihir Raj and Vandan Mujadia and Baban Gain and Pruthwik Mishra and Arafat Ahsan and Parameswari Krishnamurthy and Ashwath Rao and Gurpreet Singh Josan and Preeti Dubey and Aadil Amin Kak and Anna Rao Kulkarni and Narendra VG and Sunita Arora and Rakesh Balbantray and Prasenjit Majumdar and Karunesh K Arora and Asif Ekbal and Dipti Mishra Sharma},
      year={2025},
      eprint={2509.19941},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.19941}, 
}