A localized prediction model for statistical machine translation

Abstract

In this paper, we present a novel training method for a localized phrase-based prediction model for statistical machine translation (SMT). The model predicts blocks with orientation to handle local phrase re-ordering. We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score) as well as binary features based on the block identities themselves, e.g. block bigram features. Our training algorithm can easily handle millions of features. The best system obtains a 18.6% improvement over the baseline on a standard Arabic-English translation task.