The NiuTrans Machine Translation Systems for WMT19
Bei Li, Yinqiao Li, Chen Xu, Ye Lin, Jiqiang Liu, Hui Liu, Ziyang Wang, Yuhao Zhang, Nuo Xu, Zeyang Wang, Kai Feng, Hexuan Chen, Tengbo Liu, Yanyang Li, Qiang Wang, Tong Xiao, Jingbo Zhu
Correct Metadata for
Abstract
This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks. We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS sub-track. Our systems were built on Deep Transformer and several back-translation methods. Iterative knowledge distillation and ensemble+reranking were also employed to obtain stronger models. Our unsupervised submissions were based on NMT enhanced by SMT. As a result, we achieved the highest BLEU scores in {KK↔EN, GU→EN} directions, ranking 2nd in {RU→EN, DE↔CS} and 3rd in {ZH→EN, LT→EN, EN→RU, EN↔DE} among all constrained submissions.- Anthology ID:
- W19-5325
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 257–266
- Language:
- URL:
- https://aclanthology.org/W19-5325/
- DOI:
- 10.18653/v1/W19-5325
- Bibkey:
- Cite (ACL):
- Bei Li, Yinqiao Li, Chen Xu, Ye Lin, Jiqiang Liu, Hui Liu, Ziyang Wang, Yuhao Zhang, Nuo Xu, Zeyang Wang, Kai Feng, Hexuan Chen, Tengbo Liu, Yanyang Li, Qiang Wang, Tong Xiao, and Jingbo Zhu. 2019. The NiuTrans Machine Translation Systems for WMT19. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 257–266, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- The NiuTrans Machine Translation Systems for WMT19 (Li et al., WMT 2019)
- Copy Citation:
- PDF:
- https://aclanthology.org/W19-5325.pdf
Export citation
@inproceedings{li-etal-2019-niutrans,
title = "The {N}iu{T}rans Machine Translation Systems for {WMT}19",
author = "Li, Bei and
Li, Yinqiao and
Xu, Chen and
Lin, Ye and
Liu, Jiqiang and
Liu, Hui and
Wang, Ziyang and
Zhang, Yuhao and
Xu, Nuo and
Wang, Zeyang and
Feng, Kai and
Chen, Hexuan and
Liu, Tengbo and
Li, Yanyang and
Wang, Qiang and
Xiao, Tong and
Zhu, Jingbo",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5325/",
doi = "10.18653/v1/W19-5325",
pages = "257--266",
abstract = "This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks. We participated in 13 translation directions, including 11 supervised tasks, namely EN{\ensuremath{\leftrightarrow}}{\{}ZH, DE, RU, KK, LT{\}}, GU{\textrightarrow}EN and the unsupervised DE{\ensuremath{\leftrightarrow}}CS sub-track. Our systems were built on Deep Transformer and several back-translation methods. Iterative knowledge distillation and ensemble+reranking were also employed to obtain stronger models. Our unsupervised submissions were based on NMT enhanced by SMT. As a result, we achieved the highest BLEU scores in {\{}KK{\ensuremath{\leftrightarrow}}EN, GU{\textrightarrow}EN{\}} directions, ranking 2nd in {\{}RU{\textrightarrow}EN, DE{\ensuremath{\leftrightarrow}}CS{\}} and 3rd in {\{}ZH{\textrightarrow}EN, LT{\textrightarrow}EN, EN{\textrightarrow}RU, EN{\ensuremath{\leftrightarrow}}DE{\}} among all constrained submissions."
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<abstract>This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks. We participated in 13 translation directions, including 11 supervised tasks, namely EN\ensuremathłeftrightarrow{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE\ensuremathłeftrightarrowCS sub-track. Our systems were built on Deep Transformer and several back-translation methods. Iterative knowledge distillation and ensemble+reranking were also employed to obtain stronger models. Our unsupervised submissions were based on NMT enhanced by SMT. As a result, we achieved the highest BLEU scores in {KK\ensuremathłeftrightarrowEN, GU→EN} directions, ranking 2nd in {RU→EN, DE\ensuremathłeftrightarrowCS} and 3rd in {ZH→EN, LT→EN, EN→RU, EN\ensuremathłeftrightarrowDE} among all constrained submissions.</abstract>
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%0 Conference Proceedings
%T The NiuTrans Machine Translation Systems for WMT19
%A Li, Bei
%A Li, Yinqiao
%A Xu, Chen
%A Lin, Ye
%A Liu, Jiqiang
%A Liu, Hui
%A Wang, Ziyang
%A Zhang, Yuhao
%A Xu, Nuo
%A Wang, Zeyang
%A Feng, Kai
%A Chen, Hexuan
%A Liu, Tengbo
%A Li, Yanyang
%A Wang, Qiang
%A Xiao, Tong
%A Zhu, Jingbo
%Y Bojar, Ondřej
%Y Chatterjee, Rajen
%Y Federmann, Christian
%Y Fishel, Mark
%Y Graham, Yvette
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Martins, André
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F li-etal-2019-niutrans
%X This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks. We participated in 13 translation directions, including 11 supervised tasks, namely EN\ensuremathłeftrightarrow{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE\ensuremathłeftrightarrowCS sub-track. Our systems were built on Deep Transformer and several back-translation methods. Iterative knowledge distillation and ensemble+reranking were also employed to obtain stronger models. Our unsupervised submissions were based on NMT enhanced by SMT. As a result, we achieved the highest BLEU scores in {KK\ensuremathłeftrightarrowEN, GU→EN} directions, ranking 2nd in {RU→EN, DE\ensuremathłeftrightarrowCS} and 3rd in {ZH→EN, LT→EN, EN→RU, EN\ensuremathłeftrightarrowDE} among all constrained submissions.
%R 10.18653/v1/W19-5325
%U https://aclanthology.org/W19-5325/
%U https://doi.org/10.18653/v1/W19-5325
%P 257-266Markdown (Informal)
[The NiuTrans Machine Translation Systems for WMT19](https://aclanthology.org/W19-5325/) (Li et al., WMT 2019)
- The NiuTrans Machine Translation Systems for WMT19 (Li et al., WMT 2019)
ACL
- Bei Li, Yinqiao Li, Chen Xu, Ye Lin, Jiqiang Liu, Hui Liu, Ziyang Wang, Yuhao Zhang, Nuo Xu, Zeyang Wang, Kai Feng, Hexuan Chen, Tengbo Liu, Yanyang Li, Qiang Wang, Tong Xiao, and Jingbo Zhu. 2019. The NiuTrans Machine Translation Systems for WMT19. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 257–266, Florence, Italy. Association for Computational Linguistics.