A Localized Prediction Model for Statistical Machine Translation 
 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 . 