  In this paper, we investigate the use of linguistically motivated and
computationally efficient structured language models for reranking N-best
hypotheses in a statistical machine translation system. These language models,
developed from Constraint Dependency Grammar parses, tightly integrate
knowledge of words, morphological and lexical features, and syntactic
dependency constraints. Two structured language models are applied for N-best
rescoring, one is an almost-parsing language model, and the other utilizes more
syntactic features by explicitly modeling syntactic dependencies between words.
We also investigate effective and efficient language modeling methods to use
N-grams extracted from up to 1 teraword of web documents. We apply all these
language models for N-best re-ranking on the NIST and DARPA GALE program 2006
and 2007 machine translation evaluation tasks and find that the combination of
these language models increases the BLEU score up to 1.6% absolutely on blind
test sets.
