Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks

Translating into morphologically rich lan-guages is a particularly difficult problem in machine translation due to the high de-gree of inflectional ambiguity in the tar-get language, often only poorly captured by existing word translation models. We present a general approach that exploits source-side contexts of foreign words to improve translation prediction accuracy. Our approach is based on a probabilistic neural network which does not require lin-guistic annotation nor manual feature en-gineering. We report significant improve-ments in word translation prediction accu-racy for three morphologically rich target languages. In addition, preliminary results for integrating our approach into a large-scale English-Russian statistical machine translation system show small but statisti-cally significant improvements in transla-tion quality.