Abstract
This paper presents the team TransQuest’s participation in Sentence-Level Direct Assessment shared task in WMT 2020. We introduce a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. The proposed methods achieve state-of-the-art results surpassing the results obtained by OpenKiwi, the baseline used in the shared task. We further fine tune the QE framework by performing ensemble and data augmentation. Our approach is the winning solution in all of the language pairs according to the WMT 2020 official results.- Anthology ID:
- 2020.wmt-1.122
- 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:
- 1049–1055
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.122
- DOI:
- Bibkey:
- Cite (ACL):
- Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov. 2020. TransQuest at WMT2020: Sentence-Level Direct Assessment. In Proceedings of the Fifth Conference on Machine Translation, pages 1049–1055, Online. Association for Computational Linguistics.
- Cite (Informal):
- TransQuest at WMT2020: Sentence-Level Direct Assessment (Ranasinghe et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.122.pdf
- Video:
- https://slideslive.com/38939607
- Code
- tharindudr/transQuest
Export citation
@inproceedings{ranasinghe-etal-2020-transquest-wmt2020,
title = "{T}rans{Q}uest at {WMT}2020: Sentence-Level Direct Assessment",
author = "Ranasinghe, Tharindu and
Orasan, Constantin and
Mitkov, Ruslan",
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-1.122",
pages = "1049--1055",
abstract = "This paper presents the team TransQuest{'}s participation in Sentence-Level Direct Assessment shared task in WMT 2020. We introduce a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. The proposed methods achieve state-of-the-art results surpassing the results obtained by OpenKiwi, the baseline used in the shared task. We further fine tune the QE framework by performing ensemble and data augmentation. Our approach is the winning solution in all of the language pairs according to the WMT 2020 official results.",
}
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%0 Conference Proceedings %T TransQuest at WMT2020: Sentence-Level Direct Assessment %A Ranasinghe, Tharindu %A Orasan, Constantin %A Mitkov, Ruslan %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 ranasinghe-etal-2020-transquest-wmt2020 %X This paper presents the team TransQuest’s participation in Sentence-Level Direct Assessment shared task in WMT 2020. We introduce a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. The proposed methods achieve state-of-the-art results surpassing the results obtained by OpenKiwi, the baseline used in the shared task. We further fine tune the QE framework by performing ensemble and data augmentation. Our approach is the winning solution in all of the language pairs according to the WMT 2020 official results. %U https://aclanthology.org/2020.wmt-1.122 %P 1049-1055
Markdown (Informal)
[TransQuest at WMT2020: Sentence-Level Direct Assessment](https://aclanthology.org/2020.wmt-1.122) (Ranasinghe et al., WMT 2020)
- TransQuest at WMT2020: Sentence-Level Direct Assessment (Ranasinghe et al., WMT 2020)
ACL
- Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov. 2020. TransQuest at WMT2020: Sentence-Level Direct Assessment. In Proceedings of the Fifth Conference on Machine Translation, pages 1049–1055, Online. Association for Computational Linguistics.