India’s Machine Translation Efforts: Bridging the Linguistic Divide

Imagine a government healthcare notice that needs to reach a farmer in rural Odisha, a weaver in Tamil Nadu, and a student in Assam – all on the same day, each in their own language. This is the everyday reality of administration and communication in a country where dozens of languages coexist. Machine translation (MT), the use of computers to convert text from one natural language to another, has emerged as one of the most ambitious answers to this challenge. Over the past three decades, researchers in India have built an impressive body of work that tries to make information flow freely across linguistic boundaries. Let us trace that journey from early rule-based experiments to today’s AI-driven national missions.

The challenge of a multilingual nation 🔗

India’s linguistic diversity is the central reason machine translation research here is both necessary and difficult. The Eighth Schedule of the Constitution currently recognises 22 scheduled languages, including Hindi, Bengali, Tamil, Telugu, Urdu, Kannada, Gujarati, Assamese, and Maithili. These languages are not minor regional variants – around 96.71% of the population speaks one of them as a mother tongue.

The full picture is even larger. The 2011 Census recorded 121 languages and 270 mother tongues, with thousands of dialects spread across 28 states and 8 Union Territories. Building a translation system is not simply a matter of swapping words. Languages from the Indo-Aryan family (such as Hindi and Bengali) and the Dravidian family (such as Tamil and Telugu) differ deeply in grammar, word order, and script.

Why this is hard for machines 🔗

Three problems make Indian-language MT especially tricky. First, word order varies. English follows a Subject-Verb-Object pattern, while most Indian languages use Subject-Object-Verb. A translation system must rearrange the entire sentence, not just substitute vocabulary. Second, morphological richness means a single root word can take many forms depending on tense, gender, number, and case. Third, ambiguity: a single word can carry multiple meanings, and resolving which one is correct requires understanding context. These problems explain why early systems struggled with polysemous verbs – words with several possible senses.

Government-funded research: the institutional backbone 🔗

Machine translation research in India did not grow in isolation. It has been driven, almost from the beginning, by government sponsorship. The Government of India has funded R&D in this area since the 1980s. A turning point came in 1986, when the Department of Electronics (now the Ministry of Electronics and Information Technology, or MeitY) launched the Knowledge-Based Computer Systems programme and set up two nodal centres for language technology research – one at the Tata Institute of Fundamental Research (TIFR) and another at the National Centre for Software Technology (NCST) in Bombay, which later became C-DAC Mumbai.

The role of IITs and C-DAC 🔗

The premier engineering institutions have been at the heart of this effort. The Indian Institutes of Technology (IITs), particularly IIT Kanpur, pioneered the earliest serious MT projects. The Centre for Development of Advanced Computing (C-DAC), a research organisation under MeitY, took up the engineering challenge of turning prototypes into usable tools. C-DAC has developed several solutions for translating English into Hindi and other Indian languages including Assamese, Bengali, Malayalam, Nepali, Punjabi, Telugu, and Urdu.

MANTRA and Anuvadaksha 🔗

One of C-DAC’s flagship contributions is MANTRA (Machine Assisted Translation Tool), developed by the Applied Artificial Intelligence Group at C-DAC Pune. It uses the Tree Adjoining Grammar formalism and translates text from English to Hindi. A specialised version, MANTRA-Rajbhasha, was sponsored by the Department of Official Language under the Ministry of Home Affairs. It handles documents in domains such as personnel administration, finance, agriculture, healthcare, and banking, and is used across various ministries and government departments. Notably, MANTRA was even recognised internationally, becoming part of a 1999 innovation collection at the Smithsonian Institution.

Under the later Technology Development for Indian Languages (TDIL) programme, C-DAC Pune led a consortium to build Anuvadaksha, which translates from English into eight Indian languages – Bengali, Bodo, Gujarati, Hindi, Marathi, Oriya, Tamil, and Urdu – across the domains of agriculture, healthcare, and tourism. It combined multiple translation engines and a sophisticated pre-processing pipeline including morphological analysis, part-of-speech tagging, and word-sense disambiguation.

ANGLABHARTI and ANUBHARTI: the foundational systems 🔗

If any single research effort can be called the cornerstone of Indian MT, it is the work of Professor R. M. K. Sinha at IIT Kanpur, who is widely regarded as a pioneer of machine translation research in India.

How ANGLABHARTI works 🔗

The ANGLABHARTI project was launched in 1991 for machine-aided translation from English to Indian languages, primarily Hindi. The first prototype was actually built for English to Tamil, with a more comprehensive English-to-Hindi system following later. ANGLABHARTI uses a pattern-directed rule-based approach. Instead of translating directly into each target language separately, the system first analyses the English source and generates an intermediate representation called a “pseudo-target” or pseudo-Interlingua, which can then be applied to a whole group of Indian languages. Word-sense ambiguity in the source sentence is handled using semantic tags. A separate text generator then converts this intermediate form into the actual target language.

This design was clever for a multilingual country: the hard work of analysing English is done once, and the result can be reused to produce several Indian languages, reducing duplicated effort.

How ANUBHARTI works 🔗

ANUBHARTI takes a different route. It is based on an example-based (and later hybridised) approach to machine-aided translation. Rather than relying only on hand-written grammar rules, ANUBHARTI draws on a database of previously translated examples. This approach works particularly well for similar languages, such as translation among Indian languages, where the word order often stays the same and fewer structural adjustments are needed. The example base is built statistically from a corpus of text.

The second generation 🔗

Both architectures evolved significantly. In 2004, phase II of development was launched, producing ANGLABHARTI-II and ANUBHARTI-II. These versions were hybrids, blending rule-based and example-based paradigms. ANGLABHARTI-II added an automated pre-editing module that paraphrases or even fragments an input sentence into more easily translatable pieces, along with a “failure analysis” module that uses heuristics to figure out what went wrong when a translation failed. A related system, AnglaHindi, was developed specifically for English-to-Hindi translation and could accept unconstrained text. Reports indicated the systems produced acceptable results in more than 90% of cases, with the main weakness remaining the resolution of polysemous verbs.

The AI era: from rules to neural networks 🔗

The systems described so far belong largely to what researchers call the early and mid phases of Indian MT. The mid-phase ran from around 2005 to 2016-17, after which the field shifted decisively towards Neural Machine Translation (NMT) – an approach where deep learning models learn to translate by training on large volumes of parallel text rather than following explicit grammar rules.

Bhashini: a national mission 🔗

The most significant recent development is Bhashini, short for “Bhasha Interface for India.” Launched by the Prime Minister in July 2022 under the National Language Translation Mission, it is run by MeitY through the Digital India Corporation. Bhashini aims to provide language technology as a digital public good, offering AI-based translation services across more than 22 Indian languages in both text and voice forms.

The mission’s defining feature is its commitment to open-source AI models that startups, developers, and institutions can freely use to build their own applications. Bhashini has already been integrated into real government platforms – for example, the e-Shram portal was upgraded from just four languages to all 22 scheduled languages, and the e-Gram Swaraj platform now serves citizens in their own languages.

The role of data and academic partnerships 🔗

Modern translation quality depends heavily on data, and this is where the AI era differs sharply from the rule-based past. Neural systems need vast quantities of parallel corpora – pairs of sentences in two languages that mean the same thing. AI4Bharat, a research initiative at IIT Madras, supports Bhashini’s data efforts by collecting transcribed speech data from over 400 districts and building parallel corpora of millions of translation pairs across all 22 scheduled languages.

This points to one of the great strengths of the current approach: data-sharing. By treating language data and AI models as shared public resources rather than locked-away products, the ecosystem allows everyone – government, academia, and startups – to build on the same foundation. Industry has joined in too, with collaborations such as IIT Bombay’s work on indigenous large AI models that integrate with Bhashini.

Future prospects 🔗

The trajectory of Indian MT research suggests a few clear directions. First, quality will keep improving as more parallel data is gathered for low-resource languages – those for which little digital text currently exists. Second, voice and multimodal translation is becoming central, recognising that many citizens prefer to speak rather than type. Third, the digital public goods model means innovation is likely to come from a wide network of contributors rather than a single institution.

The persistent challenges remain real, though. Handling regional dialects, preserving meaning in culturally specific expressions, and ensuring that smaller languages are not left behind will require sustained effort. The journey from a rule-based prototype at IIT Kanpur in 1991 to an AI-powered national mission shows how far the field has travelled – and how much the goal of seamless communication across India’s linguistic landscape still depends on continued research, collaboration, and shared data.

What do you think? As neural systems trained on large datasets increasingly replace carefully hand-crafted rule-based systems like ANGLABHARTI, do we risk losing valuable linguistic insights that those earlier approaches captured? And if translation models are built mainly on data from widely spoken languages, how can we make sure India’s smaller languages and dialects receive the same quality of service?

References

1. https://www.mha.gov.in/sites/default/files/EighthSchedule_19052017.pdf
2. https://www.manoramayearbook.in/current-affairs/india/2025/01/08/scheduled-languages-upsc-explained.html
3. https://byjus.com/free-ias-prep/list-of-languages-in-the-8th-schedule/
4. https://www.psa.gov.in/article/machine-translation-national-scenario/3550
5. https://www.cdac.in/index.aspx?id=mc_mat_machine_aided_translation
6. https://www.cdac.in/index.aspx?id=mc_mat_mantra_rajbhasha
7. https://aclanthology.org/2005.mtsummit-posters.21.pdf
8. https://thescipub.com/pdf/jcssp.2010.1111.1116.pdf
9. https://www.indiascienceandtechnology.gov.in/st-visions/national-mission/national-mission-natural-language-translation-bhashini
10. https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=2093333&reg=3&lang=2
11. https://ai4bharat.iitm.ac.in/