Word translation prediction for morphologically rich languages with bilingual neural networks

Abstract

Translating into morphologically rich languages is a particularly difficult problem in machine translation due to the high degree of inflectional ambiguity in the target 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 linguistic annotation nor manual feature engineering. We report significant improvements in word translation prediction accuracy for three morphologically rich target languages. In addition, preliminary results for integrating our approach into a largescale English-Russian statistical machine translation system show small but statistically significant improvements in translation quality.