@inproceedings{yang-etal-2020-hw,
title = "{HW}-{TSC}{'}s Participation at {WMT} 2020 Automatic Post Editing Shared Task",
author = "Yang, Hao and
Wang, Minghan and
Wei, Daimeng and
Shang, Hengchao and
Guo, Jiaxin and
Li, Zongyao and
Lei, Lizhi and
Qin, Ying and
Tao, Shimin and
Sun, Shiliang and
Chen, Yimeng",
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-1.85",
pages = "797--802",
abstract = "The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-trained on WMT 2019 and WMT 2020 News Translation corpora, and fine-tuned on the APE corpus. Bottleneck Adapter Layers are integrated into the model to prevent over-fitting. We further collect external translations as the augmented MT candidates to improve the performance. The experiment demonstrates that pre-trained NMT models are effective when fine-tuning with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our system achieves competitive results on both directions in the final evaluation.",
}
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<abstract>The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-trained on WMT 2019 and WMT 2020 News Translation corpora, and fine-tuned on the APE corpus. Bottleneck Adapter Layers are integrated into the model to prevent over-fitting. We further collect external translations as the augmented MT candidates to improve the performance. The experiment demonstrates that pre-trained NMT models are effective when fine-tuning with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our system achieves competitive results on both directions in the final evaluation.</abstract>
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%0 Conference Proceedings
%T HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task
%A Yang, Hao
%A Wang, Minghan
%A Wei, Daimeng
%A Shang, Hengchao
%A Guo, Jiaxin
%A Li, Zongyao
%A Lei, Lizhi
%A Qin, Ying
%A Tao, Shimin
%A Sun, Shiliang
%A Chen, Yimeng
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F yang-etal-2020-hw
%X The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-trained on WMT 2019 and WMT 2020 News Translation corpora, and fine-tuned on the APE corpus. Bottleneck Adapter Layers are integrated into the model to prevent over-fitting. We further collect external translations as the augmented MT candidates to improve the performance. The experiment demonstrates that pre-trained NMT models are effective when fine-tuning with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our system achieves competitive results on both directions in the final evaluation.
%U https://aclanthology.org/2020.wmt-1.85
%P 797-802
Markdown (Informal)
[HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task](https://aclanthology.org/2020.wmt-1.85) (Yang et al., WMT 2020)
ACL
- Hao Yang, Minghan Wang, Daimeng Wei, Hengchao Shang, Jiaxin Guo, Zongyao Li, Lizhi Lei, Ying Qin, Shimin Tao, Shiliang Sun, and Yimeng Chen. 2020. HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task. In Proceedings of the Fifth Conference on Machine Translation, pages 797–802, Online. Association for Computational Linguistics.