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NLP and Indian Languages: Building Models that Speak Bharatiya

Tvara Thinking

NLP and Indian Languages: Building Models that Speak Bharatiya

Discover NLP in Indian languages, AI solutions for Hindi, Tamil, Bengali & more. Research, challenges, and future of multilingual NLP in India.

Where NLP ends, India begins.

Most NLP guides assume clean sentences in a single script. India replies with five scripts, two dialects, and a dash of English, inside the same sentence.

Welcome to the messy, vibrant, multilingual reality of Indian language NLP.

Why Indian Languages Are Different (and Hard)

Multi-script, Morphology Richness, Code-Mixed Text

  • Same language, multiple scripts (Konkani in Devanagari, Roman, Kannada).
  • Agglutinative grammar in Dravidian languages creates word forms unseen in training.
  • Everyday use blends English: "Kal ek meeting fix karte hai" (Hinglish).

Sparse Data for Low-Resource Languages

Hindi and Bengali have decent corpora. But Bhojpuri, Manipuri, or Santali? Data is scarce. Many languages spoken by millions are still "low-resource."

Data Strategies

Community Corpora, Government Datasets, Broadcast Transcripts

Progress depends on shared corpora. Initiatives like AI4Bharat and government-backed datasets provide text, but quality varies. Broadcast news, parliamentary transcripts, and subtitles are treasure troves when cleaned.

Cleaning Code-Mix, Normalizing Spellings

One word: chaos. Kitna may appear as kitna, kinna, qitna. Code-mix adds how much kitna hoga? Effective preprocessing normalizes without erasing meaning.

Modeling Tactics

Tokenization for Indic Scripts

Byte-level BPE (as in GPT-style models) works, but sentencepiece with Indic-aware normalization handles script variance better.

Multilingual vs Language-Specific Models

  • Multilingual: Cross-lingual transfer works for Hindi ↔ Marathi or Tamil ↔ Telugu.
  • Language-specific: Necessary for nuanced morphology and cultural idioms.

Transliteration Bridges and Romanized Inputs

Most Indians type in Romanized scripts (Tum kahan ho?). Models must learn transliteration equivalence across inputs.

Evaluation That Matches Reality

Benchmarks for Code-Mix, Dialects, Colloquialisms

Traditional benchmarks don't reflect how Indians actually text. Evaluations must include:

  • Code-mixed Hinglish, Tanglish.
  • Dialectal variations.
  • Everyday colloquialisms ("timepass," "setting," "jugaad").

Human-in-the-Loop for Intent and NER in Noisy Text

Noisy inputs need human annotators. Hybrid pipelines, AI for scale, humans for nuance, ensure intent detection and NER aren't lost in translation.

Practical Use Cases

  • Customer Support: Multilingual chatbots that respond in the user's language of choice.
  • Government Services: Schemes explained in local dialects.
  • Agricultural Advisories: Weather alerts in Telugu, pest control tips in Marathi.
  • Healthcare Triage: Symptom checkers that understand code-mixed patient queries.

Build vs Buy for Startups

When Open Models Suffice

For Hindi or Tamil with standard use cases, off-the-shelf multilingual models (like IndicBERT, mT5) may suffice.

When to Fine-Tune Privately

Sensitive domains (finance, health, legal) demand privacy, fine-tuning, and deployment within your VPC. Data boundaries are as important as accuracy.

Bottom Line

India's linguistic landscape breaks default NLP assumptions. Solving it means:

  • Better corpora.
  • Transliteration-aware tokenizers.
  • Evaluation benchmarks rooted in real-world usage.

The future of NLP in India is not about scaling English. It's about building systems that speak Bharatiya.