Building datasets for Indian languages that actually work

A product that handles Hindi well can still fail on the Hindi people actually type, which mixes English words, uses Latin script and follows spoken rhythm rather than formal grammar.

What makes it difficult

• The same language written in native script and in Latin transliteration
• Sentences that switch between two languages mid clause
• Wide dialect variation inside a single named language
• Limited existing corpora for several widely spoken languages

Collect the register you need

Formal published text and everyday messaging are different registers. A support workflow prepared only with news articles will misread the way customers write. Decide which register the product meets and collect that one.

Native reviewers per language

Reviewing Marathi with a Hindi speaker because the scripts look similar produces confident errors. Every language needs its own reviewers, and regional dialects benefit from reviewers from that region.

Practical checklist

• Define the acceptance rule before any volume starts
• Review a small pilot before committing the full budget
• Track errors by category, language and reviewer
• Keep consent, source notes and version history with the files

Before you ask for a quote

A clear brief saves days. Share a sample file, target language or region, expected volume, deadline, quality threshold and any privacy restrictions. A supplier can then price the work on real effort rather than assumptions.

• Which languages, markets or user groups must be represented?
• What format does the final file need to arrive in?
• Who will approve ambiguous cases during the pilot?