For the last several years, there is an MT industry trend of combining two approaches. Statistical MT aspires to use the linguistic data for translation quality improvement, while rule-based engines look for the application of statistical techniques in their technology.

In 2010, PROMT introduced the Hybrid version of its engine — PROMT DeepHybrid, which combines the flexibility and predictability of rule-based MT with the fluency and lexical acumen of statistical MT.

Key Components:

The PROMT DeepHybrid Engine already has the baseline rule-based quality. Further, it harvests from the client's TMs only those items that truly improve MT quality.

When a PROMT DeepHybrid profile is trained, the following components are automatically created:

When translating, the PROMT DeepHybrid engine generates several translation candidates on all levels of translation process (this is why it is called Deep Hybrid), evaluates every candidate against the LM, and finally selects the best (most probable) output.

Key Advantages:

PROMT DeepHybrid was initially designed for the Localization Industry and Language Service Providers (LSPs) but also works for any client wishing to translate large text volumes on a regular basis and that has amassed enough content for engine training.