  Statistical machine translation models have made great progress in improving
the translation quality. However, the existing models predict the target
translation with only the source- and target-side local context information. In
practice, distinguishing good translations from bad ones does not only depend
on the local features, but also rely on the global sentence-level information.
In this paper, we explore the source-side global sentence-level features for
target-side local translation prediction. We propose a novel
bilingually-constrained chunk-based convolutional neural network to learn
sentence semantic representations. With the sentence-level feature
representation, we further design a feed-forward neural network to better
predict translations using both local and global information. The large-scale
experiments show that our method can obtain substantial improvements in
translation quality over the strong baseline: the hierarchical phrase-based
translation model augmented with the neural network joint model.
