We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5.
In line with the syntax-based model of REF ) that transforms a source language parse tree into a target language string for Japanese-English translation, linguistically motivated syntactic features have been directly incorporated into both modeling and decoding.
A Syntax-Based Statistical Translation Model
Annual Meeting Of The Association For Computational Linguistics
Computer Science