摘要：AbstractIn this paper, we present a reordering model based on Maximum Entropy with local and non-local features. This model is extended from a hierarchical reordering model with PBSMT [1], which integrates rich syntactic information directly in decoder as local and non-local features of Maximum Entropy model. The advantages of this model are (1) maintaining the strength of phrase based approach with a hierarchical reordering model, (2) many kinds of rich linguistic information integrated in PBSMT as local and non-local features of MaxEntropy model. The experiment results with English-Vietnamese pair showed that our approach achieves significant improvements over the system which uses a lexical hierarchical reordering model [1].
关键词：Machine Translation;Statistical Machine Translation;Hierarchical Reordering Model
