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

ACL ID D14-1183
Title Large-scale Reordering Model for Statistical Machine Translation using Dual Multinomial Logistic Regression
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

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.