Bilingual segmentation for phrasetable pruning in Statistical Machine Translation

Statistical machine translation systems have greatly improved in the last years. However, this boost in performance usu-ally comes at a high computational cost, yielding systems that are often not suitable for integration in hand-held or real-time devices. We describe a novel technique for reducing such cost by performing a Viterbi-style selection of the parameters of the translation model. We present results with finite state transducers and phrase-based models showing a 98 % reduction of the number of parameters and a 15-fold in-crease in translation speed without any sig-nificant loss in translation quality.