Paper: Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks

ACL ID D14-1175
Title Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

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 statist...