Paper: Feature-Rich Discriminative Phrase Rescoring for SMT

ACL ID C10-1056
Title Feature-Rich Discriminative Phrase Rescoring for SMT
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

This paper proposes a new approach to phrase rescoring for statistical machine translation (SMT). A set of novel fea- tures capturing the translingual equiva- lence between a source and a target phrase pair are introduced. These features are combined with linear regression model and neural network to predict the quality score of the phrase translation pair. These phrase scores are used to dis- criminatively rescore the baseline MT system’s phrase library: boost good phrase translations while prune bad ones. This approach not only significantly im- proves machine translation quality, but also reduces the model size by a consid- erable margin.