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arXiv · 1704.06393

Neural System Combination for Machine Translation

Long Zhou·Wenpeng Hu·Jiajun Zhang·Chengqing Zong
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Abstract

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.

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Long Zhou, Wenpeng Hu, Jiajun Zhang, Chengqing Zong. 2017-04-21. Neural System Combination for Machine Translation. https://arxiv.org/abs/1704.06393

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