<p>In this paper we describe a generalized dependency tree language model for machine translation. We consider in detail the question of how to define tree-based n-grams, or 't-treelets', and thoroughly explore the strengths and weaknesses of our approach by evaluating the effect on translation quality for nine major languages. In addition, we show that it is possible to attain a significant improvement in translation quality for even non-structured machine translation by reranking filtered parses of k-best string output. </p>