Towards a Dynamic Linkage of Example-based and Rule-based Machine Translation

Abstract

This paper reports on a series of experiments which aim at integrating Example-based Machine Translation and Translation Memories with Rule-based Machine Translation. We start by examining the potentials of each MT paradigm in terms of system-internal and system-external parameters. Whereas the system-external parameters include the expected translation quality and translation coverage, system-internal parameters relate to adaptability and recall of translation units. We prefer a dynamic linkage of different MT paradigms where the sharing of labor amongst the modules involved, such as segmentation and segment translation, is decided dynamically during runtime. We motivate the communication of linguistically rich data structures between the different components in a hybrid system and show that this linkage leads to better translation results and improves the customization possibilities of the system.