Probabilistic inference for machine translation

Abstract

We advance the state-of-the-art for discriminatively trained machine translation systems by presenting novel probabilistic inference and search methods for synchronous grammars. By approximating the intractable space of all candidate translations produced by intersecting an ngram language model with a synchronous grammar, we are able to train and decode models incorporating millions of sparse, heterogeneous features. Further, we demonstrate the power of the discriminative training paradigm by extracting structured syntactic features, and achieving increases in translation performance. © 2008 Association for Computational Linguistics.