Paper: Improved Alignment Models For Statistical Machine Translation

ACL ID W99-0604
Title Improved Alignment Models For Statistical Machine Translation
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 1999
Authors

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 transla- tion model is decomposed into a lexical and an alignment model. We describe two different ap- proaches 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 differ- ent 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, 6000- word vocabulary) ...