Paper: A Markov Model of Machine Translation using Non-parametric Bayesian Inference

ACL ID P13-1033
Title A Markov Model of Machine Translation using Non-parametric Bayesian Inference
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Most modern machine translation systems use phrase pairs as translation units, al- lowing for accurate modelling of phrase- internal translation and reordering. How- ever phrase-based approaches are much less able to model sentence level effects between different phrase-pairs. We pro- pose a new model to address this im- balance, based on a word-based Markov model of translation which generates tar- get translations left-to-right. Our model encodes word and phrase level phenom- ena by conditioning translation decisions on previous decisions and uses a hierar- chical Pitman-Yor Process prior to pro- vide dynamic adaptive smoothing. This mechanism implicitly supports not only traditional phrase pairs, but also gapping phrases which are non-consecutive in the source. Our experiments on Chines...