Cross-lingual similarity evaluation for rare Indian pairs
A global AI research lab needed cross-lingual semantic textual similarity evaluation for Santali and Oriya paired with Hindi, languages where the pool of trained evaluators is extremely thin.
Santali to Hindi, Oriya to Hindi - 5,000+ prompts evaluated - Same-day correction across 8 files
110,000+
native linguists and AI data contributors
Founder-reported combined network · 4 Oct 2026
300+
languages across active service lines
4,500+
dialects and regional variants
1,000+
organizations served since 2015
Measured outcomes
Cross-lingual similarity evaluation
Cross-lingual similarity evaluation
- Client
- confidential global AI research lab
- Service
- Cross-lingual semantic similarity (XSTS) evaluation
- Languages
- Santali to Hindi, Oriya to Hindi
- Volume
- 5,000+ prompts evaluated
A global AI research lab needed cross-lingual semantic textual similarity evaluation for Santali and Oriya paired with Hindi.
Santali has roughly 7.5 million speakers but an extremely limited pool of trained linguists, so scoring similarity across domains demanded both deep language expertise and evaluation training.
The problem to solve
Scoring semantic similarity between a source and its translation is not translation work: it needs evaluators trained in the methodology and fluent enough to judge meaning as well as words.
For a language like Santali, the constraint is supply: very few linguists combine native fluency with evaluation training, so quality control has to be tight from the first batch.
How AI data services was delivered
MoniSa deployed native Santali and Oriya linguists validated for dominant proficiency in both source and target, and shared consolidated linguist feedback before production began.
Validated native linguists
Evaluators were validated for dominant proficiency in both the source and target language.
Feedback before production
Consolidated feedback from native Santali linguists was shared before production started, not after.
Same-day correction
When QA flagged a scoring issue and a domain mismatch, corrections were applied across all 8 files the same day.
What was delivered on this engagement?
5,000+ prompts were evaluated across two rare Indian pairs, with all QA feedback resolved within 48 hours and the delivery accepted through the agreed review path.
| Languages | Santali to Hindi, Oriya to Hindi |
|---|---|
| Volume | 5,000+ prompts evaluated |
| QA resolution | Same-day correction across 8 files |
| Outcome | accepted through the agreed review path |
What supported the result
Why this team fit the assignment
Rare-pair similarity evaluation needs native linguists who also know the methodology, which is exactly the supply that is hard to find for Santali.
What decided the result
Sharing linguist feedback before production and resolving QA the same day is what kept a thin-supply project on track.
What buyers can reuse
- For rare-language evaluation, the bottleneck is trained native linguists, not methodology alone.
- Proactive feedback and same-day QA resolution kept a thin-supply project from stalling.
- The evidence keeps the client details confidential and attributes the metrics only to this engagement.
Work connected to Cross-lingual similarity evaluation
Related services, buying guidance and language coverage
AI data services and buyer guidance
Languages listed
- Santali
- Oriya
- Hindi
- Rare Indian language pairs
More proof
Compare this case with LLM training data coverage and Rare-language evaluation at scale to judge whether the operating pattern fits your brief.
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Cross-lingual similarity evaluation questions
What was delivered on this engagement?
Languages: Santali to Hindi, Oriya to Hindi. Volume: 5,000+ prompts evaluated. QA resolution: Same-day correction across 8 files
Which control supported the Cross-lingual similarity evaluation result?
Sharing linguist feedback before production and resolving QA the same day is what kept a thin-supply project on track.
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