Rich source-side context for statistical machine translation

We explore the augmentation of statistical ma-chine translation models with features of the context of each phrase to be translated. This work extends several existing threads of re-search in statistical MT, including the use of context in example-based machine translation (Carl and Way, 2003) and the incorporation of word sense disambiguation into a translation model (Chan et al., 2007). The context fea-tures we consider use surrounding words and part-of-speech tags, local syntactic structure, and other properties of the source language sentence to help predict each phrase’s transla-tion. Our approach requires very little compu-tation beyond the standard phrase extraction algorithm and scales well to large data sce-narios. We report significant improvements in automatic evaluation scores for Chinese-to-English and English-to-German translation, and also describe our entry in the WMT-08 shared task based on this approach.