MTEQA at WMT21 Metrics Shared Task
Mateusz Krubiński, Erfan Ghadery, Marie-Francine Moens, Pavel Pecina
Abstract
In this paper, we describe our submission to the WMT 2021 Metrics Shared Task. We use the automatically-generated questions and answers to evaluate the quality of Machine Translation (MT) systems. Our submission builds upon the recently proposed MTEQA framework. Experiments on WMT20 evaluation datasets show that at the system-level the MTEQA metric achieves performance comparable with other state-of-the-art solutions, while considering only a certain amount of information from the whole translation.- Anthology ID:
- 2021.wmt-1.110
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1024–1029
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.110
- DOI:
- Bibkey:
- Cite (ACL):
- Mateusz Krubiński, Erfan Ghadery, Marie-Francine Moens, and Pavel Pecina. 2021. MTEQA at WMT21 Metrics Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 1024–1029, Online. Association for Computational Linguistics.
- Cite (Informal):
- MTEQA at WMT21 Metrics Shared Task (Krubiński et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.110.pdf
Export citation
@inproceedings{krubinski-etal-2021-mteqa,
title = "{MTEQA} at {WMT}21 Metrics Shared Task",
author = "Krubi{\'n}ski, Mateusz and
Ghadery, Erfan and
Moens, Marie-Francine and
Pecina, Pavel",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.110",
pages = "1024--1029",
abstract = "In this paper, we describe our submission to the WMT 2021 Metrics Shared Task. We use the automatically-generated questions and answers to evaluate the quality of Machine Translation (MT) systems. Our submission builds upon the recently proposed MTEQA framework. Experiments on WMT20 evaluation datasets show that at the system-level the MTEQA metric achieves performance comparable with other state-of-the-art solutions, while considering only a certain amount of information from the whole translation.",
}
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%0 Conference Proceedings %T MTEQA at WMT21 Metrics Shared Task %A Krubiński, Mateusz %A Ghadery, Erfan %A Moens, Marie-Francine %A Pecina, Pavel %Y Barrault, Loic %Y Bojar, Ondrej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussa, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Kocmi, Tom %Y Martins, Andre %Y Morishita, Makoto %Y Monz, Christof %S Proceedings of the Sixth Conference on Machine Translation %D 2021 %8 November %I Association for Computational Linguistics %C Online %F krubinski-etal-2021-mteqa %X In this paper, we describe our submission to the WMT 2021 Metrics Shared Task. We use the automatically-generated questions and answers to evaluate the quality of Machine Translation (MT) systems. Our submission builds upon the recently proposed MTEQA framework. Experiments on WMT20 evaluation datasets show that at the system-level the MTEQA metric achieves performance comparable with other state-of-the-art solutions, while considering only a certain amount of information from the whole translation. %U https://aclanthology.org/2021.wmt-1.110 %P 1024-1029
Markdown (Informal)
[MTEQA at WMT21 Metrics Shared Task](https://aclanthology.org/2021.wmt-1.110) (Krubiński et al., WMT 2021)
- MTEQA at WMT21 Metrics Shared Task (Krubiński et al., WMT 2021)
ACL
- Mateusz Krubiński, Erfan Ghadery, Marie-Francine Moens, and Pavel Pecina. 2021. MTEQA at WMT21 Metrics Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 1024–1029, Online. Association for Computational Linguistics.