Can AI Translate 2,970 Language Pairs? Inside the Challenge of Multilingual Translation
Most people think AI translation is a solved problem.
Need to translate English to Spanish? There are dozens of tools that can do it in seconds.
But what happens when the translation is between Santali and Malayalam? Or Bodo and Tamil? Or Maithili and Urdu?
Suddenly, the challenge becomes much harder.
While major languages receive most of the attention, thousands of language combinations remain underserved by modern AI systems. Building a translation model that works across hundreds or thousands of language pairs requires far more than simply adding new vocabulary.
It requires solving one of the most complex problems in artificial intelligence: helping machines understand meaning across vastly different languages, scripts, cultures, and contexts.
Today, some multilingual AI systems support thousands of translation directions. At Shunya Labs, our translation stack supports 55+ Indian languages and 2,970 directed language pairs, spanning 15 scripts and more than a billion speakers. The journey to get there reveals just how difficult multilingual AI really is.
Why Language Pairs Matter More in AI Translation
When companies talk about translation systems, they usually mention the number of languages supported.
That number tells only part of the story.
The real challenge lies in language pairs.
If a system supports 10 languages, it doesn’t create 10 translation paths.
It creates 90.
If it supports 20 languages, it creates 380 possible translation directions.
At 55 languages, the number jumps to 2,970 directed pairs.
| Languages Supported | Possible Translation Pairs |
|---|---|
| 10 | 90 |
| 20 | 380 |
| 30 | 870 |
| 55 | 2,970 |
Every additional language increases complexity exponentially.
This is why supporting dozens of languages is not simply a scaling problem. It becomes an entirely different engineering challenge.
Why AI Translation Still Struggles With Many Languages
Translation systems perform best when they have access to massive amounts of training data.
For English, French, Spanish, and Mandarin, that data is relatively easy to find. For languages such as Santali, Garo, Kurukh, or Chhattisgarhi, it is not.
Many low-resource languages have:
- Limited digital content
- Few publicly available datasets
- Multiple writing systems
- Regional variations
- Limited benchmark evaluations
As a result, many global translation systems focus primarily on widely spoken languages while hundreds of regional languages remain underrepresented.
This creates a gap between what AI can do for major languages and what it can do for everyone else.
The Hidden Challenge of Indian Languages
India alone has hundreds of languages and dialects.
Many people switch between languages naturally during conversation.
A customer may begin a sentence in Hindi and finish it in English.
A support interaction may involve regional terms that do not appear in standard language datasets.
Traditional translation systems often struggle in these situations because they were designed for clean, single-language inputs.
Modern multilingual models must handle:
- Multiple scripts
- Code-switched speech
- Regional vocabulary
- Cultural context
- Low-resource languages
This is one reason translation remains one of the most difficult problems in AI despite recent advances.
How Modern Translation Models Actually Work
Most modern translation systems follow a similar process.
Step 1: Understand the Source Language
The model first analyzes the incoming text or speech and identifies its meaning.
This goes beyond word-for-word conversion.
The system must understand context, grammar, intent, and relationships between words.
Step 2: Create a Shared Representation
Modern multilingual models convert meaning into an internal representation that is independent of any specific language.
Think of it as a language-neutral understanding layer.
Instead of translating directly from Language A to Language B, the model first captures meaning.
Step 3: Generate the Target Language
The model then reconstructs that meaning in the destination language while preserving context and intent.
This step is particularly challenging for languages with different grammatical structures.
Step 4: Maintain Quality Across Thousands of Pairs
The final challenge is consistency.
A system may perform well on Hindi-to-English translation but struggle on Assamese-to-Urdu.
Maintaining quality across thousands of language combinations requires extensive training and evaluation.
Inside a Translation System Supporting 2,970 Language Pairs
Shunya Labs‘ Vāķ was designed to address these challenges across Indian and regional languages.
The platform supports:
- 55+ languages
- 2,970 directed language pairs
- 15 writing systems
- Text-to-Text translation
- Speech-to-Text translation
- Speech-to-Speech translation
- Streaming input and output support
Unlike traditional translation workflows that rely on multiple disconnected tools, modern systems combine speech recognition, translation, and speech synthesis into a unified pipeline.
This allows users to move seamlessly from spoken input to spoken output in another language.
Why Low-Resource Languages Matter
The future of AI translation will not be decided by English, Spanish, or French.
It will be decided by languages that have historically been overlooked.
Several languages supported within the Shunya translation ecosystem received some of their earliest machine translation coverage through dedicated model development efforts.
These include languages such as:
- Santali
- Bodo
- Garo
- Magahi
- Chhattisgarhi
For many communities, language technology is not simply about convenience.
It is about digital inclusion.
When AI systems cannot understand a language, speakers of that language are effectively excluded from digital services.
This is one of the most important reasons multilingual AI continues to evolve.
The Future of Translation Is Multilingual and Multimodal
Translation is no longer limited to text.
Modern systems increasingly support:
- Text-to-Text
- Speech-to-Text
- Speech-to-Speech
- Real-time translation
- Voice agents
- Conversational AI
As businesses expand across regions and languages, demand for real-time multilingual communication continues to grow.
Healthcare providers, governments, financial institutions, customer support teams, and educational platforms all face the same challenge:
How do you communicate effectively with people who speak different languages?
The answer increasingly involves AI systems capable of understanding and translating language at scale.
Contact us to know more.
Translation Is Still One of AI’s Hardest Problems
The ability to support 2,970 language pairs sounds impressive.
The reality is that every additional language introduces new challenges involving data, accuracy, context, and evaluation.
Translation is not simply about replacing words.
It is about preserving meaning.
As AI expands into more regions and languages, the most important breakthroughs may not happen in widely spoken languages. They may happen in the languages that technology has historically ignored.
The future of AI translation will belong to systems that can bridge those gaps and make digital communication accessible to everyone.
Frequently Asked Questions
What is a language pair in machine translation?
A language pair refers to a translation direction between two languages. For example, English-to-Hindi and Hindi-to-English are considered separate language pairs.
Why are low-resource languages difficult for AI translation?
Low-resource languages often have limited training data, fewer benchmarks, and less digital content available for model training.
What is multilingual AI translation?
Multilingual AI translation refers to systems capable of translating between many languages using a shared machine learning model rather than separate models for each language pair.
What is speech-to-speech translation?
Speech-to-speech translation converts spoken language into another spoken language using speech recognition, translation, and text-to-speech technologies.
Why is multilingual translation important?
Multilingual translation improves accessibility, expands digital inclusion, and helps businesses serve users across diverse linguistic regions.
Bio: Navvya works at the intersection of product strategy and applied AI research at Shunya Labs. With a background in human behaviour and communication, she writes about the people, markets, and technology behind voice AI, with a particular focus on how speech interfaces are reshaping access across emerging markets.