emnlp emnlp2013 emnlp2013-186 emnlp2013-186-reference knowledge-graph by maker-knowledge-mining
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Author: Victor Chahuneau ; Eva Schlinger ; Noah A. Smith ; Chris Dyer
Abstract: Translation into morphologically rich languages is an important but recalcitrant problem in MT. We present a simple and effective approach that deals with the problem in two phases. First, a discriminative model is learned to predict inflections of target words from rich source-side annotations. Then, this model is used to create additional sentencespecific word- and phrase-level translations that are added to a standard translation model as “synthetic” phrases. Our approach relies on morphological analysis of the target language, but we show that an unsupervised Bayesian model of morphology can successfully be used in place of a supervised analyzer. We report significant improvements in translation quality when translating from English to Russian, Hebrew and Swahili.