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).
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).