@inproceedings{hu-etal-2020-niutrans-system,
title = "The {N}iu{T}rans System for the {WMT}20 Quality Estimation Shared Task",
author = "Hu, Chi and
Liu, Hui and
Feng, Kai and
Xu, Chen and
Xu, Nuo and
Zhou, Zefan and
Yan, Shiqin and
Luo, Yingfeng and
Wang, Chenglong and
Meng, Xia and
Xiao, Tong and
Zhu, Jingbo",
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.117",
pages = "1018--1023",
abstract = "This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task. We participated in all tasks and all language pairs. We explored the combination of transfer learning, multi-task learning and model ensemble. Results on multiple tasks show that deep transformer machine translation models and multilingual pretraining methods significantly improve translation quality estimation performance. Our system achieved remarkable results in multiple level tasks, e.g., our submissions obtained the best results on all tracks in the sentence-level Direct Assessment task.",
}
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<abstract>This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task. We participated in all tasks and all language pairs. We explored the combination of transfer learning, multi-task learning and model ensemble. Results on multiple tasks show that deep transformer machine translation models and multilingual pretraining methods significantly improve translation quality estimation performance. Our system achieved remarkable results in multiple level tasks, e.g., our submissions obtained the best results on all tracks in the sentence-level Direct Assessment task.</abstract>
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%0 Conference Proceedings
%T The NiuTrans System for the WMT20 Quality Estimation Shared Task
%A Hu, Chi
%A Liu, Hui
%A Feng, Kai
%A Xu, Chen
%A Xu, Nuo
%A Zhou, Zefan
%A Yan, Shiqin
%A Luo, Yingfeng
%A Wang, Chenglong
%A Meng, Xia
%A Xiao, Tong
%A Zhu, Jingbo
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F hu-etal-2020-niutrans-system
%X This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task. We participated in all tasks and all language pairs. We explored the combination of transfer learning, multi-task learning and model ensemble. Results on multiple tasks show that deep transformer machine translation models and multilingual pretraining methods significantly improve translation quality estimation performance. Our system achieved remarkable results in multiple level tasks, e.g., our submissions obtained the best results on all tracks in the sentence-level Direct Assessment task.
%U https://aclanthology.org/2020.wmt-1.117
%P 1018-1023
Markdown (Informal)
[The NiuTrans System for the WMT20 Quality Estimation Shared Task](https://aclanthology.org/2020.wmt-1.117) (Hu et al., WMT 2020)
ACL
- Chi Hu, Hui Liu, Kai Feng, Chen Xu, Nuo Xu, Zefan Zhou, Shiqin Yan, Yingfeng Luo, Chenglong Wang, Xia Meng, Tong Xiao, and Jingbo Zhu. 2020. The NiuTrans System for the WMT20 Quality Estimation Shared Task. In Proceedings of the Fifth Conference on Machine Translation, pages 1018–1023, Online. Association for Computational Linguistics.