Syntax encapsulated phrase model for statistical machine translation

In the past few years, much attention has been paid on extending phrase-based statistical machine translation with syntactic structures. In this paper we introduce a novel syntax encapsulated phrase(SEP) model, in which treebank tag sequences are employed to decorate the bilingual phrase pairs. We use tag sequences, instead of phrase pairs, to train the lexicalized reordering model. Since the number of treebank tags is much smaller than the number of words, the tag sequence based reordering model is smaller and more accurate than the phrase based reordering model. Experiments were carried out on four types of models: the phrase model, the hierarchical phrase model, the POS tag encapsulated phrase(PTEP) model and the syntactic tag encapsulated phrase(STEP) model. The STEP model obtained higher BLEU-4 score than other models on NIST 2005 MT task.