  Current Neural Machine Translation (NMT) employs a language-specific encoder
to represent the source sentence and adopts a language-specific decoder to
generate target translation. This language-dependent design leads to
large-scale network parameters and makes the duality of the parallel data
underutilized. To address the problem, we propose in this paper a
language-independent representor to replace the encoder and decoder by using
weight sharing. This shared representor can not only reduce large portion of
network parameters, but also facilitate us to fully explore the language
duality by jointly training source-to-target, target-to-source, left-to-right
and right-to-left translations within a multi-task learning framework.
Experiments show that our proposed framework can obtain significant
improvements over conventional NMT models on resource-rich and low-resource
translation tasks with only a quarter of parameters.
