CASICT-DCU Neural Machine Translation Systems for WMT17

We participated in the WMT 2016 shared news translation task on English ↔ Chinese language pair. Our systems are based on the encoder-decoder neural machine translation model with the attention mechanism. We employ the Gated Recurrent Unit (GRU) with the linear associative connection to build deep encoder and address the unknown words with the dictionary replace approach. The dictionaries are extracted from the parallel training data with unsupervised word alignment method. In the decoding procedure, the translation probabilities of the target word from different models are averagely combined as the ensemble strategy. In this paper, we introduce our systems from data preprocessing to post-editing in details.