English to Indian languages parallel dataset

This dataset presents a high-quality human-translated parallel corpus comprising English sentences aligned with their translations into six major Indian languages. The resource has been meticulously curated to support research and development in machine translation.

The parallel corpus is domain-balanced, with content systematically selected and translated across five key domains that reflect real-world linguistic diversity and practical relevance:

1. Governance and Policy (Primary Domain): Texts from administrative communications, government schemes, policies, and citizen-centric materials.

2. Science and Technology: Passages covering emerging technologies, innovation, and scientific awareness content.

3. Education: Educational materials, curriculum-based texts, and pedagogical content.

4. Health: Public health information, medical advisories, and awareness literature.

5. Agriculture: Farmer outreach, crop management, and rural development-related texts.

All translations have been performed and validated by professional human translators, ensuring high linguistic fidelity, semantic equivalence, and domain consistency across languages. Rigorous quality checks were followed to maintain alignment accuracy and contextual relevance.

This corpus has been developed under the EILMT (English to Indian Languages Machine Translation) consortium, operating within the framework of the Mission Bhashini initiative of the Government of India. The effort aligns with the national vision of enabling language inclusivity, accessibility of digital content, and fostering multilingual AI technologies.

The dataset serves as a benchmark resource for building and evaluating translation systems, domain adaptation studies, and linguistic resource development for Indian languages—thereby contributing to the broader goals of linguistic empowerment and digital inclusivity in India.

Consortia Members

The project is being carried out in collaboration with the following institutions:

Centre for Development of Advanced Computing (C-DAC), Noida

Centre for Development of Advanced Computing (C-DAC), Pune

Indian Institute of Technology (IIT) Bombay

IIIT Hyderabad

AU-KBC, Anna University Chennai

Banasthali Vidyapith

C-DAC Bengaluru

C-DAC Trivandrum

Dharmsinh Desai University, Gujarat

IIIT Bhubaneswar, Odisha

For more information, visit project's github at: https://github.com/eilmt/NLTM-EILMT