We describe stochastic models of local phrase movement that can be incorporated into a Statistical Machine Translation (SMT) system. These models provide properly formulated, non-deficient, probability distributions over reordered phrase sequences. They are implemented by Weighted Finite State Transducers. We describe EM-style parameter re-estimation procedures based on phrase alignment under the complete translation model incorporating reordering. Our experiments show that the reordering model yields substantial improvements in translation performance on Arabic-to-English and Chinese-to-English MT tasks. We also show that the procedure scales as the bitext size is increased.
REF describe a class of reordering constraints and models that can be encoded in finite state transducers.
Local Phrase Reordering Models For Statistical Machine Translation
Human Language Technology Conference And Empirical Methods In Natural Language Processing
Computer Science