Using Feature Structures to Improve Verb Translation in English-to-German Statistical MT

SCFG-based statistical MT models have proven effective for modelling syntactic aspects of translation, but still suffer problems of overgeneration. The production of German verbal complexes is particularly challenging since highly discontiguous constructions must be formed consistently, often from multiple independent rules. We extend a strong SCFG-based string-to-tree model to incorporate a rich feature-structure based representation of German verbal complex types and compare verbal complex production against that of the reference translations, finding a high baseline rate of error. By developing model features that use source-side information to influence the production of verbal complexes we are able to substantially improve the type accuracy as compared to the reference.