The motivation for developing hybrid machine translation systems stems from the failure of any single technique to achieve a satisfactory level of accuracy.
Most commonly, these systems use statistical and rule-based translation subsystems,[1] but other combinations have been explored.
[2] This approach involves using statistical data to generate lexical and syntactic rules.
As a result, this technique has had the most success in domain-specific applications, and has the same difficulties with domain adaptation as many statistical machine translation systems.
This technique is used to limit the amount of information a statistical system need consider, significantly reducing the processing power required.