Neural System Combination for Machine Translation
Long Zhou, Wenpeng Hu, Jiajun Zhang, Chengqing Zong · 2017
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 Chineseto-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.