Direct Translation Model 2

This paper presents a maximum entropy machine translation system using a minimal set of translation blocks (phrase-pairs). While recent phrase-based statistical machine translation (SMT) systems achieve significant improvement over the original source-channel statistical translation models, they 1) use a large inventory of blocks which have significant overlap and 2) limit the use of training to just a few parameters (on the order of ten). In contrast, we show that our proposed minimalist system (DTM2) achieves equal or better performance by 1) recasting the translation problem in the traditional statistical modeling approach using blocks with no overlap and 2) relying on training most system parameters (on the order of millions or larger). The new model is a direct translation model (DTM) formulation which allows easy integration of additional/alternative views of both source and target sentences such as segmentation for a source language such as Arabic, part-of-speech of both source and target, etc. We show improvements over a state-of-the-art phrase-based decoder in Arabic-English translation.