Use cases

What quality prediction is for, and what it isn't

Quality prediction is for accelerating translation safely. More efficiency, same quality.

Concretely, quality prediction is for accelerating manual human post-editing workflows that are too slow or too expensive, by cutting out agencies or manual work for millions of segments that would not be edited anyway.

The goal of accelerating post-editing workflows could be to grow capacity, to speed up turnaround time or to save costs. It could even be part of a plan to improve quality by shifting more content from raw machine translation to accelerated post-editing, or from external vendors to in-house, to expand into more languages, to provide more service-level tiers or to reduce workflow steps.

Quality prediction is not for one-off or low-value use cases that are offline or only measure work, like comparing machine translation engines, cleaning translation memories, estimating post-editing effort at the document level or annotating at the word level. Although those other, subtly different use cases do have some value because they are based on real daily problems, they would actually require fundamentally different systems.

Quality prediction is only for actually accelerating post-editing work safely, directly in the main production workflow, by automatically verifying as many segments as safely possible, so that they can skip manual human post-editing.

The value is in fully skipping a manual step for exactly the segments that can be safely skipped, because a segment is the main unit of human cognitive effort. (We cannot verify a translation without reading the whole sentence.) And this precedent is already well-established for decades: the translation memory, legacy workflows systems, CAT tools and payment contracts work at the segment level.

Requirements	System A	System B	System C
Safety Same human final quality	✓
Convenience Works easily with any TMS, MT or agency	✓	✓
Alignment Full focus and no conflict of interest	✓		✓
Success stories Working in the real world	✓

A practical guide to machine translation quality prediction