Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars 
 Syntax-based statistical machine translation (MT) aims at applying statistical models to structured data . In this paper, we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar . Synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees . We first introduce our approach to inducing such a grammar from parallel corpora . Second, we describe the graphical model for the machine translation task , which can also be viewed as a stochastic tree-to-tree transducer . We introduce a polynomial time decoding algorithm for the model . We evaluate the outputs of our MT system using the NIST and Bleu automatic MT evaluation software . The result shows that our system outperforms the baseline system based on the IBM models in both translation speed and quality . 