Neural System Combination for Machine Translation

Long Zhou, Wenpeng Hu, Jiajun Zhang, Chengqing Zong


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.
Anthology ID:
P17-2060
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
378–384
Language:
URL:
https://aclanthology.org/P17-2060
DOI:
10.18653/v1/P17-2060
Bibkey:
Cite (ACL):
Long Zhou, Wenpeng Hu, Jiajun Zhang, and Chengqing Zong. 2017. Neural System Combination for Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 378–384, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Neural System Combination for Machine Translation (Zhou et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2060.pdf
Poster:
 P17-2060.Poster.pdf