@inproceedings{wang-etal-2020-hw-tscs,
title = "{HW}-{TSC}{'}s Participation at {WMT} 2020 Quality Estimation Shared Task",
author = "Wang, Minghan and
Yang, Hao and
Shang, Hengchao and
Wei, Daimeng and
Guo, Jiaxin and
Lei, Lizhi and
Qin, Ying and
Tao, Shimin and
Sun, Shiliang and
Chen, Yimeng and
Li, Liangyou",
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-1.123",
pages = "1056--1061",
abstract = "This paper presents our work in the WMT 2020 Word and Sentence-Level Post-Editing Quality Estimation (QE) Shared Task. Our system follows standard Predictor-Estimator architecture, with a pre-trained Transformer as the Predictor, and specific classifiers and regressors as Estimators. We integrate Bottleneck Adapter Layers in the Predictor to improve the transfer learning efficiency and prevent from over-fitting. At the same time, we jointly train the word- and sentence-level tasks with a unified model with multitask learning. Pseudo-PE assisted QE (PEAQE) is proposed, resulting in significant improvements on the performance. Our submissions achieve competitive result in word/sentence-level sub-tasks for both of En-De/Zh language pairs.",
}
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<abstract>This paper presents our work in the WMT 2020 Word and Sentence-Level Post-Editing Quality Estimation (QE) Shared Task. Our system follows standard Predictor-Estimator architecture, with a pre-trained Transformer as the Predictor, and specific classifiers and regressors as Estimators. We integrate Bottleneck Adapter Layers in the Predictor to improve the transfer learning efficiency and prevent from over-fitting. At the same time, we jointly train the word- and sentence-level tasks with a unified model with multitask learning. Pseudo-PE assisted QE (PEAQE) is proposed, resulting in significant improvements on the performance. Our submissions achieve competitive result in word/sentence-level sub-tasks for both of En-De/Zh language pairs.</abstract>
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%0 Conference Proceedings
%T HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task
%A Wang, Minghan
%A Yang, Hao
%A Shang, Hengchao
%A Wei, Daimeng
%A Guo, Jiaxin
%A Lei, Lizhi
%A Qin, Ying
%A Tao, Shimin
%A Sun, Shiliang
%A Chen, Yimeng
%A Li, Liangyou
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-hw-tscs
%X This paper presents our work in the WMT 2020 Word and Sentence-Level Post-Editing Quality Estimation (QE) Shared Task. Our system follows standard Predictor-Estimator architecture, with a pre-trained Transformer as the Predictor, and specific classifiers and regressors as Estimators. We integrate Bottleneck Adapter Layers in the Predictor to improve the transfer learning efficiency and prevent from over-fitting. At the same time, we jointly train the word- and sentence-level tasks with a unified model with multitask learning. Pseudo-PE assisted QE (PEAQE) is proposed, resulting in significant improvements on the performance. Our submissions achieve competitive result in word/sentence-level sub-tasks for both of En-De/Zh language pairs.
%U https://aclanthology.org/2020.wmt-1.123
%P 1056-1061
Markdown (Informal)
[HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task](https://aclanthology.org/2020.wmt-1.123) (Wang et al., WMT 2020)
ACL
- Minghan Wang, Hao Yang, Hengchao Shang, Daimeng Wei, Jiaxin Guo, Lizhi Lei, Ying Qin, Shimin Tao, Shiliang Sun, Yimeng Chen, and Liangyou Li. 2020. HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task. In Proceedings of the Fifth Conference on Machine Translation, pages 1056–1061, Online. Association for Computational Linguistics.