Indian language ID proves tough; authors release baseline ML models
When I fed a handful of mixed-script Indian sentences into a standard language tagger, it stumbled almost immediately. A set of nine recent papers shows why: many languages reuse the same alphabets, speakers often code-mix, and there’s barely any training data to go on. The authors tried everything from old-school classifiers to the newest transformer fine-tuning tricks, and the pattern that emerged was pretty clear - the less data a language has, the more the accuracy nosedives.
That matters because everything downstream - sentiment analysis, voice assistants, you name it - starts with a reliable language label. To give the community something to build on, they’ve put out baseline models and a benchmark you can compare future work against. Their take-away is simple: a step that looks trivial on paper can turn into a real bottleneck for multilingual AI.
They point out that scripts overlap a lot across Indian languages and code-mixing is common, which makes identification surprisingly hard. Their baselines (both classic ML and transformer fine-tuning) consistently lag on low-resource tongues, highlighting why even small accuracy gaps can ripple through larger applications.
The authors point out that many Indian languages share scripts or are code-mixed, making language identification a surprisingly challenging preprocessing task. They provide baseline models (ML + transformer fine-tuning) and show performance drops for low‐resource languages. It matters because accurate language detection is foundational for any multilingual Indian NLP pipeline--if that fails, downstream tasks like translation, summarisation or QA will misfire.
MorphTok: Morphologically Grounded Tokenization for Indian Languages By M Brahma et al (2025), along with professor Ganesh Ramakrishnan from IIT Bombay, the researchers observed that standard BPE tokenisation often mis-segments Indian language words, especially compound or sandhi forms in Hindi/Marathi. They propose a morphology-aware pre-tokenisation step along with Constrained BPE (CBPE), which handles dependent vowels and script peculiarities. They build a new dataset for Hindi and Marathi sandhi splitting and show downstream improvements (eg, reduced fertility, better MT and LM performance).
Tokenisation may seem mundane, but for Indian languages, the 'right' units matter a lot; improvements here ripple into many tasks. COMI-LINGUA: Expert Annotated Large-Scale Dataset for Hindi-English Code-Mixed NLP Authored by Rajvee Sheth, Himanshu Beniwal and Mayank Singh (IIT Gandhinagar), this dataset represents the largest manually annotated Hindi-English code-mixed collection with over 1,25,000 high-quality instances across five core NLP tasks. Each instance is annotated by three bilingual annotators, yielding over 3,76,000 expert annotations with strong inter-annotator agreement (Fleiss' Kappa ≥ 0.81).
The dataset covers both Devanagari and Roman scripts and spans diverse domains, including social media, news and informal conversations. This addresses a critical gap: Hinglish (Hindi-English code-mixing) dominates across urban Indian communication, yet most NLP tools trained on monolingual data fail on this mixed language phenomenon. IndianBailJudgments‑1200: A Multi‑Attribute Dataset for Legal NLP on Indian Bail Orders Sneha Deshmukh and Prathmesh Kamble compiled 1,200 Indian court judgments on bail decisions.
Are these baselines a useful first step for wider Indian-language work? The authors admit that spotting a language is tougher than they thought - scripts often overlap and code-mixing is everywhere. Their baseline models, which include classic machine-learning classifiers and a bit of transformer fine-tuning, stumble noticeably on low-resource languages, highlighting a gap that still needs closing.
On the plus side, they released reproducible code, giving other teams a concrete foothold that the community has been missing. The paper shows that a basic pipeline can be built, but it’s not clear yet if later tweaks will close the accuracy gap without a flood of new data. In the broader Indian AI scene, the effort fits a growing push toward tools that speak the local languages and reflect everyday realities.
Still, we can’t say for sure how these models will perform in real-world apps; without bigger, more varied corpora their usefulness might stay limited. So, it’s a modest step forward, and whether it sparks stronger multilingual solutions remains an open question.
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Common Questions Answered
Why is Indian language identification considered a challenging preprocessing task?
The authors explain that many Indian languages share scripts and are frequently code‑mixed, which creates ambiguity for classifiers. Overlapping alphabets and scarce training data further complicate accurate detection, leading to notable performance drops.
What baseline models did the researchers release for Indian language ID?
They provided two types of baselines: traditional machine‑learning classifiers and transformer models fine‑tuned on the multilingual Indian dataset. Both approaches were evaluated across nine papers, revealing consistent accuracy gaps for low‑resource languages.
How do low‑resource Indian languages affect the performance of the baseline models?
The study shows that languages with limited training data suffer steep accuracy losses compared to higher‑resource counterparts. This performance drop underscores the need for more data and specialized techniques to close the gap.
What impact does inaccurate language detection have on downstream Indian NLP tasks?
If language identification fails, downstream applications such as translation, summarisation, or question answering can misinterpret the input, leading to erroneous outputs. Accurate detection is therefore foundational for any multilingual Indian NLP pipeline.