In this paper we describe an approach to target language modeling which is based on a large treebank. We assume a bag of bags as input for the target language gener-ation component, leaving it up to this com-ponent to decide upon word and phrase or-der. An experiment with Dutch as target language shows that this approach to can-didate translation reranking outperforms standard n-gram modeling, when measur-ing output quality with BLEU, NIST, and TER metrics. 1