Low-Resource Challenges in Building Effective NLP Models for English to Assamese Translation
If you’ve ever tried using a translation tool for English to Assamese translation, chances are, the result wasn’t quite what you expected. It might have been okay for a basic sentence. But the moment the text became a little more complex, say, with idioms, cultural references, or longer structures, the quality probably dropped. Why does this happen? Let’s unpack it.
What makes Assamese "low-resource"?Here’s the thing: Assamese isn’t a small language. It’s spoken by over 15 million people, mainly in Assam and nearby regions. The language has a long literary tradition, rich with poetry, songs, and history. But in the world of machine learning, it’s called a low-resource language. Why? Because of the kind of digital data needed to train AI models, large, clean, parallel text datasets simply don’t exist at the scale required.
For languages like French or Spanish, there are millions of sentence pairs aligned with English. These come from everything from movie subtitles to EU documents. Assamese doesn’t have that kind of ready-made resource bank. And without data, even the smartest AI doesn’t have much to learn from.
More than just data, grammar, and structure play a roleLet’s look at this from a language perspective. English follows a Subject-Verb-Object (SVO) order. Assamese? It’s typically Subject-Object-Verb (SOV). That alone creates challenges when converting word order naturally. But there’s more.
Assamese, like many Indian languages, has complex morphology. This means words change form based on tense, gender, number, and other grammatical factors. For a machine learning model, that’s tricky. It needs to grasp not only vocabulary but also how suffixes and prefixes shift meaning. With limited examples to learn from, it’s easy for the system to misfire.
Why is parallel data hard to come by?For starters, unlike in Europe, where bilingual records are common thanks to institutions like the EU, we don’t have an equivalent volume of English-Assamese documents. Assamese literature and government records do exist, of course. But much of this content hasn’t been digitized or aligned properly with English translations. And what is available is often inconsistent in style or spelling.
Subtitles, media content, and user-generated text could help. But again, there just hasn’t been enough large-scale effort (yet) to produce and share this data in machine-readable form.
How are researchers tackling this?One common method is transfer learning. Imagine you train a model in Hindi or Bengali (languages with more resources) and then fine-tune it using the small amount of Assamese data you do have. The model can find patterns that are the same across different languages, which can help.
Back-translation is another smart trick. In this case, a crude model is used to translate monolingual Assamese text into English. That English text, along with the original Assamese, creates new training data. The goal is to add more data in a clever, synthetic fashion.
There are also projects that depend on people in the neighborhood. Some services are asking native speakers to translate small parts of the text, which will slowly build up a dataset. This method takes longer, but it usually gives you good, trustworthy data.
Why does it matter?In this day and age, everyone should be able to get online. When English to Assamese translation tools doesn't operate correctly, millions of people miss out on things like being able to read internet content, use government services, or just talk to individuals who speak different languages.
Assam is very important to India's northeast. It's not simply a technical problem to support its language online; it's also about making sure everyone can access it and keeping cultural identity alive in the digital world.
The way forwardWhat excellent news! There are more and more efforts. More people are paying attention, from government programs to academic research, from crowdsourcing to new ideas from the commercial sector. But to really solve the problem, we'll need to combine technology, language skills, and help from the community.
To construct good English-to-Assamese translation systems, you need to value the language enough to put money into it. And when that happens, the good things will go beyond only translation.