IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages

Jay Gala, Pranjal A Chitale, A K Raghavan, Varun Gumma, Sumanth Doddapaneni, Aswanth Kumar M, Janki Atul Nawale, Anupama Sujatha, Ratish Puduppully, Vivek Raghavan, Pratyush Kumar, Mitesh M Khapra, Raj Dabre, Anoop Kunchukuttan

Abstract: India has a rich linguistic landscape, with languages from 4 major language families spoken by over a billion people. 22 of these languages listed in the Constitution of India (referred to as scheduled languages) are the focus of this work. Given the linguistic diversity, high-quality and accessible Machine Translation (MT) systems are essential in a country like India. Before this work, there was (i) no parallel training data spanning all 22 languages, (ii) no robust benchmarks covering all these languages and containing content relevant to India, and (iii) no existing translation models that support all 22 scheduled languages of India. In this work, we aim to address this gap by focusing on the missing pieces required for enabling wide, easy, and open access to good machine translation systems for all 22 scheduled Indian languages. We identify four key areas of improvement: curating and creating larger training datasets, creating diverse and high-quality benchmarks, training multilingual models, and releasing models with open access. Our first contribution is the release of the Bharat Parallel Corpus Collection (BPCC), the largest publicly available parallel corpora for Indic languages. BPCC contains a total of 230M bitext pairs, of which a total of 126M were newly added, including 644K manually translated sentence pairs created as part of this work. Our second contribution is the release of the first $n$-way parallel benchmark covering all 22 Indian languages, featuring diverse domains, Indian-origin content, and conversational test sets. Next, we present IndicTrans2, the first translation model to support all 22 languages, surpassing existing models in performance on multiple existing and new benchmarks created as a part of this work. Lastly, to promote accessibility and collaboration, we release our models and associated data with permissive licenses at https://github.com/AI4Bharat/IndicTrans2.

Submission Length: Long submission (more than 12 pages of main content)

Changes Since Last Submission:

- Deanonymization and added links to our artifacts.
- Added details about M2M models (Changes in section 7.5, added results of M2M model as well as its comparison to Pivoting).
- Some typographical changes and citation fixes.
- Added evaluation results for the updated version of seamless M4T v2 in Appendix B3.
- Minor changes in limitations and conclusion section due to release of M2M model.

Code: https://github.com/AI4Bharat/IndicTrans2

Assigned Action Editor: ~W_Ronny_Huang1

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 1501