C., Saunders

M., Niranjan

Ni, Y

S., Szedmak

We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to capture phrase reorderings using a structure learning framework. On both the reordering classiﬁcation and a Chinese-to-English translation task, we show improved performance over a baseline SMT system.We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to capture phrase reorderings using a structure learning framework. On both the reordering classiﬁcation and a Chinese-to-English translation task, we show improved performance over a baseline SMT system

Handling phrase reordering for machine translation

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