XMU neural machine translation systems for WMT 17

Abstract

This paper describes the Neural Machine Translation systems of Xiamen University for the translation tasks of WMT 17. Our systems are based on the Encoder-Decoder framework with attention. We participated in three directions of shared news translation tasks: English?German and Chinese?English. We experimented with deep architectures, different segmentation models, synthetic training data and target-bidirectional translation models. Experiments show that all methods can give substantial improvements.