Paper: Translating into Morphologically Rich Languages with Synthetic Phrases

ACL ID D13-1174
Title Translating into Morphologically Rich Languages with Synthetic Phrases
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

Translation into morphologically rich lan- guages is an important but recalcitrant prob- lem in MT. We present a simple and effec- tive 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 sentence- specific word- and phrase-level translations that are added to a standard translation model as ?synthetic? phrases. Our approach re- lies on morphological analysis of the target language, but we show that an unsupervised Bayesian model of morphology can success- fully be used in place of a supervised analyzer. We report significant improvements in transla- tion quality when translating from English to Russian, Hebrew and Swahili.