Deep grammars in a tree labeling approach to syntax-based statistical machine translation
Mark Hopkins, Jonas Kuhn · 2007
In this paper, we propose a new syntaxbased machine translation (MT) approach based on reducing the MT task to a tree-labeling task, which is further decomposed into a sequence of simple decisions for which discriminative classifiers can be trained. The approach is very flexible and we believe that it is particularly well-suited for exploiting the linguistic knowledge encoded in deep grammars whenever possible, while at the same time taking advantage of data-based techniques that have proven a powerful basis for MT, as recent advances in statistical MT show.