Abstract

In this paper, we describe improved alignment models for statistical machine translation. The statistical translation approach uses two types of information: a translation model and a lan- guage model. The language model used is a bigram or general m-gram model. The translation model is decomposed into a lexical and an alignment model. We describe two different approaches for statistical translation and present experimental results. The first approach is based on dependencies between single words, the second approach explicitly takes shallow phrase structures into account, using two different alignment levels: a phrase level alignment between phrases and a word level alignment between single words. We present results us- ing the Verbmobil task (German-English, 6000word vocabulary) which is a limited-domain spoken-language task. The experimental tests were performed on both the text transcription and the speech recognizer output.

Citations