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Local Translation Prediction with Global Sentence Representation
Zhang, Jiajun; Zhang, Dakun; Hao, Jie
Conference NameIJCAI
Conference Date2015-7
Conference PlaceBuenos Aires, Argentina

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 chunkbased convolutional neural network to learn sentence semantic representations. With the sentencelevel 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.

Document Type会议论文
Recommended Citation
GB/T 7714
Zhang, Jiajun,Zhang, Dakun,Hao, Jie. Local Translation Prediction with Global Sentence Representation[C],2015.
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