How hybrid post-editing works

With the hybrid post-editing workflow, you can translate up to 10x more. Hybrid post-editing is a fit for post-editing workflows where many machine translations are currently not getting edited at all by human translators. Let's walk through how it works in your translation management system (TMS).

Quality prediction

The key technology is translation quality prediction. The ModelFront API predicts if a machine translation is good or bad. It learns from your post-editing data, to reflect your domain, terminology, style - even for specific brands, products or quality tiers.

Hybrid translation

Easy integration

ModelFront is available as an integration or connector in all major TMSes. See integrations and connectors → ModelFront also provides an API that can be integrated into any TMS or other applications.

Hybrid translation

1, 2, 3

For hybrid, the TMS first calls the MT API for new segments, as usual. Then it calls the ModelFront API, to get a quality prediction for each new segment. Now the TMS uses the good MT segments like 100% TM matches.

Like 100% TM matches

The good MT segments can be "translated", "approved", "confirmed" or even locked. They're included only for context. Only the remaining bad MT segments get full human post-editing.

Hybrid translation

Easy for translators

There's no change needed in the CAT, and no change needed for the human translators. You can also choose to let human translators see, filter and sort by the score in the CAT.

ModelFront

With ModelFront integrated into your TMS, you can use good MT like 100% TM matches. The hybrid translation workflow combines human quality and machine speed.