In statistical machine translation, an effective way to improve the translation quality is to regularize the posterior probabilities of translation hypotheses according to the information of N-best list. In this paper, we present a novel approach to improve the final translation result by dynamically augmenting the translation scores of hypotheses that derived from the N-best translation candidates. The proposed model was trained on a general domain UM-Corpus and evaluated on IWSLT Chinese-English TED Talk data under the configurations of document level translation and sentence level translation respectively. Empirical results real that sentence level translation model outperforms the document level and the baseline system.