Large-scale Reordering Model for Statistical Machine Translation using Dual Multinomial Logistic Regression

Phrase reordering is a challenge for statis-tical machine translation systems. Posing phrase movements as a prediction prob-lem using contextual features modeled by maximum entropy-based classifier is su-perior 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 clas-sification problems. Using the dual prob-lem to multinomial logistic regression, we managed to shrink the training data while iterating and produce significant saving in computation and memory while preserv-ing the accuracy.