Date of Original Version
Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP)
Copyright 2013 Association for Computational Linguistics
Abstract or Description
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.
Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), 1677-1687.