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.

该论文探索了使用源语言全局句子级特征来预测目标语言本地翻译，并提出了一种新的双语限制基于块的卷积神经网络来学习语义表示，并使用局部和全局信息的前馈神经网络来更好地预测翻译质量。实验表明，该方法可以在翻译质量方面得到实质性的提高。

使用全局句子表示进行本地翻译预测