  The dominant neural machine translation (NMT) models apply unified
attentional encoder-decoder neural networks for translation. Traditionally, the
NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a
left-toright manner, leaving the target-side contexts generated from right to
left unexploited during translation. In this paper, we equip the conventional
attentional encoder-decoder NMT framework with a backward decoder, in order to
explore bidirectional decoding for NMT. Attending to the hidden state sequence
produced by the encoder, our backward decoder first learns to generate the
target-side hidden state sequence from right to left. Then, the forward decoder
performs translation in the forward direction, while in each translation
prediction timestep, it simultaneously applies two attention models to consider
the source-side and reverse target-side hidden states, respectively. With this
new architecture, our model is able to fully exploit source- and target-side
contexts to improve translation quality altogether. Experimental results on
NIST Chinese-English and WMT English-German translation tasks demonstrate that
our model achieves substantial improvements over the conventional NMT by 3.14
and 1.38 BLEU points, respectively. The source code of this work can be
obtained from https://github.com/DeepLearnXMU/ABDNMT.
