  Neural machine translation (NMT) becomes a new approach to machine
translation and generates much more fluent results compared to statistical
machine translation (SMT).
  However, SMT is usually better than NMT in translation adequacy. It is
therefore a promising direction to combine the advantages of both NMT and SMT.
  In this paper, we propose a neural system combination framework leveraging
multi-source NMT, which takes as input the outputs of NMT and SMT systems and
produces the final translation.
  Extensive experiments on the Chinese-to-English translation task show that
our model archives significant improvement by 5.3 BLEU points over the best
single system output and 3.4 BLEU points over the state-of-the-art traditional
system combination methods.
