  Neural Machine Translation (NMT) has become a popular technology in recent
years, and the encoder-decoder framework is the mainstream among all the
methods. It's obvious that the quality of the semantic representations from
encoding is very crucial and can significantly affect the performance of the
model. However, existing unidirectional source-to-target architectures may
hardly produce a language-independent representation of the text because they
rely heavily on the specific relations of the given language pairs. To
alleviate this problem, in this paper, we propose a novel Bi-Decoder Augmented
Network (BiDAN) for the neural machine translation task. Besides the original
decoder which generates the target language sequence, we add an auxiliary
decoder to generate back the source language sequence at the training time.
Since each decoder transforms the representations of the input text into its
corresponding language, jointly training with two target ends can make the
shared encoder has the potential to produce a language-independent semantic
space. We conduct extensive experiments on several NMT benchmark datasets and
the results demonstrate the effectiveness of our proposed approach.
