ANZ Bank · NVIDIA
0.82 — Gini coefficient for assessing risk
Independently reportedBanking & finance · Other · Classical ML · Pilot
Other
Microsoft · Appen
Vendor-reportedTechnology & softwareScaledEnterpriseNLP
Vendor-reported. The customer is named and the numbers are quoted from the source page, but the account comes from the vendor. No independent confirmation.
Reported by appen.com (vendor self-report). Checked against the source page on 2026-09-06. 2 of 2 figures below appear on that page word for word.
The problem
Sourcing and annotating large datasets, particularly for less frequently spoken languages, to build accurate machine translation models and address potential bias.
What was deployed
- Vendor
- Appen
- Products
- Microsoft Translator, Azure Cognitive Services
- Technique
- NLP
- Build or buy
- Bought and customised
- Deployment
- SaaS
- Data used
- large datasets from native speakers, language data directly from native speakers, annotated data
- Scale
- 110 languages
What changed
Each row is quoted from the source. Figures we could not find on the page in those words are marked — they are kept, not deleted, so you can judge them.
110Languages supported by the platform
“The platform now supports 110 languages”
Quoted word for word from the source
108Languages with data sourced and annotated by Appen
“Appen played a critical role in sourcing and annotating data for 108 of those languages.”
Quoted word for word from the source
“Appen’s breadth of language expertise and ability to source speakers, even for under-resourced languages, has allowed us to offer a wide range of languages and dialects, with Microsoft Translator.”
Difficulties and limits
- Sourcing sufficient data for rare and less-documented languages
- Finding fluent speakers or existing data for some languages
- Accurately transcribing and translating data into target languages, requiring linguistic expertise and cultural understanding across diverse alphabets, phonetic systems, and grammar
- Addressing potential translation bias, such as ensuring accurate translations for gender-ambiguous source sentences
Sources
- appen.com — vendor self-report
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