Contents
In the three decades since the internet started to change the way billions of people live and work, only two technologies had significant impact on the efficiency of human-quality translation: the translation memory and machine translation.
Now the next evolution is here: machine translation quality prediction (MTQP).
Global companies and institutions are using quality prediction to translate up to 5x more efficiently - at the same human quality - by accurately deciding which segments require human post-editing, and which don't.
Like GPT, quality prediction is built with Transformer-based multilingual LLMs (large language models) first researched in academia and companies like Microsoft and Google, but it took startups to make it accessible as a cloud product, available in translation management systems and trusted by enterprise teams translating millions or billions of words a year.
We'll cover the problem, what quality prediction is, the use cases and how to get starte
Takeaways
1. The problem: Why human-quality translation - even machine translation post-editing - is so inefficient
2. Quality prediction: What it is, and how it is different than quality evaluation like BLEU
3. Use cases: The highest value use cases, when they're a fit, ROI
4. How to get started: Build or buy, choosing a provider, customization, rollout