Phrase reordering is a challenge for statistical machine translation systems. Posing phrase movements as a prediction problem using contextual features modeled by maximum entropy-based classifier is superior to the commonly used lexicalized reordering model. However, Training this discriminative model using large-scale parallel corpus might be computationally expensive. In this paper, we explore recent advancements in solving large-scale classification problems. Using the dual problem to multinomial logistic regression, we managed to shrink the training data while iterating and produce significant saving in computation and memory while preserving the accuracy.
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