Code-Mixing Chaos: How AI Models Are Cracking Hinglish and Tanglish

By Editorial Staff |

Ask almost anyone in an Indian city to describe their day, and their sentence will likely switch between English and a regional language without a pause. This article explains what code-mixing is, why it confuses traditional AI systems, and the techniques researchers use to help models understand Hinglish, Tanglish and similar blends.

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Understanding Code-Mixing

Code-mixing happens when speakers combine words and grammar from two or more languages in a single conversation or even a single sentence. “Kal meeting cancel ho gayi, so I’m free” is a typical Hinglish example. Tanglish does the same with Tamil and English, while Benglish, Kanglish and other blends follow similar patterns.

The challenge multiplies because people often write these mixtures in Roman script. Hindi words typed in English letters have no standard spelling, so “bahut,” “bohot” and “bhot” may all mean the same thing. A system trained on clean, single-language text has no obvious way to make sense of this.

Why Traditional Models Struggle

Older language tools were built on the assumption that each document belongs to one language. A sentiment analyser might read “movie was bakwaas” and miss that “bakwaas” signals strong disapproval. A translator might try to process the whole sentence as English and produce nonsense.

Grammar adds another layer. Hinglish sentences frequently follow Hindi word order while borrowing English nouns and verbs, creating structures that do not exist in either language on its own. Without exposure to real mixed speech, models cannot learn these patterns.

Techniques That Are Making Progress

Researchers and companies are tackling code-mixing from several directions:

  1. Training on real conversations: social media posts, chat logs and transcribed speech, gathered with appropriate consent and anonymisation, expose models to authentic mixed language.
  2. Language identification at word level: tagging each word by language helps a system decide how to interpret it.
  3. Transliteration normalisation: converting varied Roman spellings into a consistent form, or into native script, reduces confusion.
  4. Multilingual pretraining: large models trained on many languages at once learn shared representations, making switches less disruptive.
  5. Synthetic data generation: creating artificial code-mixed sentences fills gaps where natural examples are scarce, though these must be checked for realism.

Evaluation is evolving too. Researchers have developed benchmarks with mixed-language tasks such as sentiment detection, question answering and named entity recognition, giving teams a way to measure whether improvements are real.

Where It Matters in Practice

Customer support chatbots, social media moderation, voice assistants and search engines all deal with code-mixed input every day. A support bot that understands “mera order abhi tak deliver nahi hua” can resolve a complaint quickly, while one that does not frustrates the customer.

Businesses building such tools should collect examples from their own users, test across regions and keep humans available for messages the system cannot parse confidently.

Summing Up

Code-mixing reflects how millions of Indians naturally communicate, and AI systems that ignore it will keep misunderstanding their users. Training on authentic mixed data, normalising spellings and building dedicated benchmarks are steadily helping models handle Hinglish, Tanglish and other blends with growing reliability.