In this work we revise the application of discriminative learning to the problem of phrase selection in Statistical Machine Translation. Inspired by common techniques used in Word Sense Disambiguation, we train classifiers based on local context to predict possible phrase translations. Our work extends that of Vickrey et al. (2005) in two main aspects. First, we move from word translation to phrase translation. Second, we move from the ‘blank-filling ’ task to the ‘full translation ’ task. We report results on a set of highly frequent source phrases, obtaining a significant improvement, specially with respect to adequacy, according to a rigorous process of manual evaluation. 1

This document describes the system developed by the Empirical MT Group at the Technical University of Catalonia, LSI Department, for the Arabic-to-English task at the