Integrating a Discriminative Classifier into Phrase-based and Hierarchical Decoding

Current state-of-the-art statistical machine translation (SMT) relies on simple feature functionswhichmake independence assumptions at the level of phrases or hierarchical rules. However, it is well-known that discriminative models can benefit from rich features extracted from the source sentence context outside of the applied phrase or hierarchical rule, which is available at decoding time. We present a framework for the open-source decoder Moses that allows discriminative models over source context to easily be trained on a large number of examples and then be included as feature functions in decoding.