Indian language ID proves tough; authors release baseline ML models
Identifying the language of a snippet of Indian text is anything but a solved problem. Researchers have assembled a collection of nine papers that, together, expose how overlapping alphabets, frequent code‑mixing, and scarce training data throw a wrench into even the most polished pipelines. Their work spans classic machine‑learning classifiers and the latest transformer fine‑tuning tricks, yet the results reveal a clear pattern: languages with limited resources suffer steep accuracy losses.
This isn’t just an academic footnote; downstream tasks—from sentiment analysis to voice assistants—rely on a reliable first‑step tag. The authors therefore release baseline models to give the community a starting point and a yardstick for future improvements. Their findings underscore why a seemingly simple preprocessing step can become a bottleneck in multilingual AI systems.
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 accura
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.
Can these baselines serve as a stepping stone for broader Indian‑language work? The authors acknowledge that language identification is harder than expected because many scripts overlap and code‑mixing is common. Their baseline models—traditional machine‑learning classifiers and transformer fine‑tuning—show clear performance drops on low‑resource languages, underscoring a gap that still needs closing.
Yet the release of reproducible code gives other teams a concrete starting point, something the community has lacked. While the paper demonstrates the feasibility of a baseline pipeline, it leaves open whether future refinements will bridge the accuracy gap without extensive data. In the wider context of Indian AI research, the work aligns with a shift toward tools that reflect local languages, laws and everyday realities.
Still, the impact of these models on real‑world applications remains uncertain; without larger, more diverse corpora, their usefulness may stay limited. The effort marks a modest advance, but whether it will catalyze more robust multilingual solutions is still unclear.
Further Reading
- ILID: Native Script Language Identification for Indian Languages - arXiv
- Advancing Indian Language Detection: A Hybrid Neural Architecture for Language Audio Classification - Sys-Core
- IWSLT 2025 Indic Track System Description Paper: Speech-to-Text Translation for Low-Resource Indian Languages - ACL Anthology
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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.