Using a Supertagged Dependency Language Model to Select a Good Translation in System Combination

We present a novel, structured language model- Supertagged Dependency Language Model to model the syntactic dependencies between words. The goal is to identify ungrammatical hypotheses from a set of candidate translations in a MT system combination framework and help select the best translation candidates using a variety of sentence-level features. We use a two-step mechanism based on constituent parsing and elementary tree extraction to obtain supertags and their dependency relations. Our experiments show that the structured language model provides significant improvement in the framework of sentence-level system combination.