@inproceedings{zhou-etal-2017-neural,
title = "Neural System Combination for Machine Translation",
author = "Zhou, Long and
Hu, Wenpeng and
Zhang, Jiajun and
Zong, Chengqing",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2060/",
doi = "10.18653/v1/P17-2060",
pages = "378--384",
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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%0 Conference Proceedings
%T Neural System Combination for Machine Translation
%A Zhou, Long
%A Hu, Wenpeng
%A Zhang, Jiajun
%A Zong, Chengqing
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F zhou-etal-2017-neural
%X 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.
%R 10.18653/v1/P17-2060
%U https://aclanthology.org/P17-2060/
%U https://doi.org/10.18653/v1/P17-2060
%P 378-384
Markdown (Informal)
[Neural System Combination for Machine Translation](https://aclanthology.org/P17-2060/) (Zhou et al., ACL 2017)
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.