Using morphology to improve Example-Based Machine Translation

MyBook is a cheap paperback edition of the original book and will be sold at uniform, low price.
This Chapter is currently unavailable for purchase.

We describe how morphological information was used in an Example-Based Arabic-to-English Machine Translation system to produce significant improvement in translation quality on both small and large corpora. We experimented with different methods of generalizing morphology to obtain more candidate source-side matches, while retaining information about the specific input to be translated. This information was then used with adaptation rules and a language model to generate context-appropriate target-side fragments, select and combine them. We outline essential differences between Statistical MT (SMT) and Example-based MT (EBMT), compare ourselves to other EBMT systems used with morphologically complex languages, and justify our choice of EBMT over SMT.


This is a required field
Please enter a valid email address