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Multilingual collection · Machine Translation

Multilingual Data Collection for Machine Translation

Parallel programmes across several Indian languages at once, run to one specification so the resulting datasets are comparable rather than a set of incompatible deliveries. Applied to machine translation, the specification is driven by one thing: human adequacy and fluency scores.

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Abstract visualisation of translation between two Indian languages — Multilingual Data Collection for Machine Translation
Service
Multilingual collection
Use case
Machine Translation
Primary metric
Human adequacy and fluency scores
01

Required data profile

  • Sentence-aligned parallel corpora
  • Register-matched to your product
  • Enforced terminology glossary
Multilingual collection — Machine Translation · written into the SOW before recordingLanguagesUp to 14 Indian languages plus Indian English in one progra…ConsistencyOne spec, one manifest schema, one QA standard across all l…BalancePer-language quotas set independently to match your deploym…ReportingSingle consolidated progress view across languagesYour values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
LanguagesUp to 14 Indian languages plus Indian English in one programme
ConsistencyOne spec, one manifest schema, one QA standard across all languages
BalancePer-language quotas set independently to match your deployment mix
ReportingSingle consolidated progress view across languages
Field recording session with a rural speaker in India — supporting multilingual data collection for machine translation
Field recording session with a rural speaker in India
03

Process

  • Master specification written once and localised per language
  • Per-language linguistic review of prompts and guides
  • Parallel fielding across studio cities
  • Central QA applying identical thresholds
  • Consolidated delivery with per-language reports
04

Metrics this feeds

  • Human adequacy and fluency scores
  • Terminology compliance rate
  • Back-translation divergence
05

Failure modes to design out

  • Pivoting everything through English
  • Post-edited machine output passed off as human translation

The hardest language sets the timeline. Low-resource languages such as Assamese and Odia should start first.

06

Deliverables

  • Per-language datasets in one schema
  • Cross-language coverage report
  • Consolidated QA summary

Frequently asked

Is multilingual collection the right service for machine translation?

It covers sentence-aligned parallel corpora. Most machine translation programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.

What languages are available?

All 14 languages in the network plus Indian English accent bands.

How is the evaluation set handled?

Collected first, from speakers disjoint from the training cohort, so improvement is measurable.

Scope multilingual collection for machine translation

Send the metric you need to move.

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