HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task
Minghan Wang, Hao Yang, Hengchao Shang, Daimeng Wei, Jiaxin Guo, Lizhi Lei, Ying Qin, Shimin Tao, Shiliang Sun, Yimeng Chen, Liangyou Li
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.- Anthology ID:
- 2020.wmt-1.123
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1056–1061
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.123
- DOI:
- Bibkey:
- Cite (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.
- Cite (Informal):
- HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task (Wang et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.123.pdf
- Video:
- https://slideslive.com/38939571
Export citation
@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",
editor = {Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
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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%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 %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %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)
- HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task (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.