Live Webinar - Optimize LLM performance through Human-AI Collaboration

Sourcing In-market Expertise for Software Localization

Published on
August 21, 2017

Sourcing In-market Expertise for Software Localization

Top Software Provider Develops Global CLDR Through Trusted Partnership

The Situation

A major international software provider focused on software localization needed to update its Unicode Common Locale Data Repository (CLDR) and find in-market representation for 66 markets where it didn’t have an internal presence. The project required locale-specific data, including date and time formatting, capitalization rules, and local currency. Strong project management was needed to ensure success.

The Solution

The software provider approached Appen for representation in the 66 smaller markets. The task was challenging due to technology limitations, political conflicts, and declining populations in their target languages.The project started with two test markets. After succeeding in those markets, the remaining marketing were added according to a ramp schedule to manage the flow of data. Two hundred participants were sourced to provide data entry, voting and forum participation.

The Results

The client was able to successfully update its Unicode Common Locale Data Repository (CLDR) for the refresh cycle with Appen’s high quality, local resources. The client also saved time and money by avoiding sending its own employees into the field for data collection. The client now counts on Appen for recurring CLDR project participation on a bi-annual basis.

Related posts

Lorem ipsum dolor sit amet, consectetur adipiscing elit.

What is Human-in-the-Loop Machine Learning?

Human-in-the-loop (HITL) is a branch of artificial intelligence that leverages both human and machine intelligence to create machine learning models. In a traditional
Read more

Deciphering AI from Human Generated Text: The Behavioral Approach

One of the most important elements of building a well-functioning AI model is consistent human feedback. When generative AI models are trained by human annotators, they serve
Read more

Data Quality: The Better the Data, the Better the Model

If your data’s not accurate, your model won’t run...properly, that is. While you may end up with a working model, it won’t function the way it was intended. The quality of
Read more

Machine Vision vs. Computer Vision — What’s the Difference?

Artificial Intelligence is an umbrella term that covers several specific technologies. In this post, we will explore machine vision (MV) vs. computer vision (CV). They both
Read more
Dec 11, 2023