Alibaba Submission for WMT18 Quality Estimation Task
Jiayi Wang, Kai Fan, Bo Li, Fengming Zhou, Boxing Chen, Yangbin Shi, Luo Si
Correct Metadata for
Abstract
The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations. This paper presents the QE Brain system, which proposes the neural Bilingual Expert model as a feature extractor based on conditional target language model with a bidirectional transformer and then processes the semantic representations of source and the translation output with a Bi-LSTM predictive model for automatic quality estimation. The system has been applied to the sentence-level scoring and ranking tasks as well as the word-level tasks for finding errors for each word in translations. An extensive set of experimental results have shown that our system outperformed the best results in WMT 2017 Quality Estimation tasks and obtained top results in WMT 2018.- Anthology ID:
- W18-6465
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 809–815
- Language:
- URL:
- https://aclanthology.org/W18-6465/
- DOI:
- 10.18653/v1/W18-6465
- Bibkey:
- Cite (ACL):
- Jiayi Wang, Kai Fan, Bo Li, Fengming Zhou, Boxing Chen, Yangbin Shi, and Luo Si. 2018. Alibaba Submission for WMT18 Quality Estimation Task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 809–815, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- Alibaba Submission for WMT18 Quality Estimation Task (Wang et al., WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6465.pdf
Export citation
@inproceedings{wang-etal-2018-alibaba,
title = "{A}libaba Submission for {WMT}18 Quality Estimation Task",
author = "Wang, Jiayi and
Fan, Kai and
Li, Bo and
Zhou, Fengming and
Chen, Boxing and
Shi, Yangbin and
Si, Luo",
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
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6465/",
doi = "10.18653/v1/W18-6465",
pages = "809--815",
abstract = "The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations. This paper presents the QE Brain system, which proposes the neural Bilingual Expert model as a feature extractor based on conditional target language model with a bidirectional transformer and then processes the semantic representations of source and the translation output with a Bi-LSTM predictive model for automatic quality estimation. The system has been applied to the sentence-level scoring and ranking tasks as well as the word-level tasks for finding errors for each word in translations. An extensive set of experimental results have shown that our system outperformed the best results in WMT 2017 Quality Estimation tasks and obtained top results in WMT 2018."
}
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<abstract>The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations. This paper presents the QE Brain system, which proposes the neural Bilingual Expert model as a feature extractor based on conditional target language model with a bidirectional transformer and then processes the semantic representations of source and the translation output with a Bi-LSTM predictive model for automatic quality estimation. The system has been applied to the sentence-level scoring and ranking tasks as well as the word-level tasks for finding errors for each word in translations. An extensive set of experimental results have shown that our system outperformed the best results in WMT 2017 Quality Estimation tasks and obtained top results in WMT 2018.</abstract>
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%0 Conference Proceedings %T Alibaba Submission for WMT18 Quality Estimation Task %A Wang, Jiayi %A Fan, Kai %A Li, Bo %A Zhou, Fengming %A Chen, Boxing %A Shi, Yangbin %A Si, Luo %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 Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Specia, Lucia %Y Turchi, Marco %Y Verspoor, Karin %S Proceedings of the Third Conference on Machine Translation: Shared Task Papers %D 2018 %8 October %I Association for Computational Linguistics %C Belgium, Brussels %F wang-etal-2018-alibaba %X The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations. This paper presents the QE Brain system, which proposes the neural Bilingual Expert model as a feature extractor based on conditional target language model with a bidirectional transformer and then processes the semantic representations of source and the translation output with a Bi-LSTM predictive model for automatic quality estimation. The system has been applied to the sentence-level scoring and ranking tasks as well as the word-level tasks for finding errors for each word in translations. An extensive set of experimental results have shown that our system outperformed the best results in WMT 2017 Quality Estimation tasks and obtained top results in WMT 2018. %R 10.18653/v1/W18-6465 %U https://aclanthology.org/W18-6465/ %U https://doi.org/10.18653/v1/W18-6465 %P 809-815
Markdown (Informal)
[Alibaba Submission for WMT18 Quality Estimation Task](https://aclanthology.org/W18-6465/) (Wang et al., WMT 2018)
- Alibaba Submission for WMT18 Quality Estimation Task (Wang et al., WMT 2018)
ACL
- Jiayi Wang, Kai Fan, Bo Li, Fengming Zhou, Boxing Chen, Yangbin Shi, and Luo Si. 2018. Alibaba Submission for WMT18 Quality Estimation Task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 809–815, Belgium, Brussels. Association for Computational Linguistics.