What is Hybrid Machine Translation?

Hybrid Machine Translation is a method of machine translation that is characterized by the use of multiple machine translation approaches within a single machine translation system. The motivation for developing hybrid machine translation systems stems from the failure of any single technique to achieve a satisfactory level of accuracy.

Although there are several forms of hybrid machine translation such as Multi-Engine, statistical rule generation and multi-pass, the most common forms are:

Rules Post-Processed by Statistics: Translations are performed using a rules based engine. Statistics are then used in an attempt to adjust/correct the output from the rules engine. This is also known as statistical smoothing and automatic post editing. This is more of a “Band-Aid” approach to machine translation where there is an attempt to improve lower quality output from an RBMT engine rather than addressing the root cause of issues.

Statistics Guided by Rules: Rules are used to pre-process data in an attempt to better guide the statistical engine. Rules are also used to post-process the statistical output to perform functions such as normalization. This approach has a lot more power, flexibility and control when translating. Many issues can be addressed at their root causes through rules that go beyond the capabilities on a statistical only approach.

There are a number of other approaches also. You can learn more about them on Wikipedia.