Correct Metadata for
Abstract
The paper presents our submission to the WMT2021 Shared Task on Quality Estimation (QE). We participate in sentence-level predictions of human judgments and post-editing effort. We propose a glass-box approach based on attention weights extracted from machine translation systems. In contrast to the previous works, we directly explore attention weight matrices without replacing them with general metrics (like entropy). We show that some of our models can be trained with a small amount of a high-cost labelled data. In the absence of training data our approach still demonstrates a moderate linear correlation, when trained with synthetic data.- Anthology ID:
- 2021.wmt-1.101
- 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:
- 955–960
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.101/
- DOI:
- Bibkey:
- Cite (ACL):
- Lisa Yankovskaya and Mark Fishel. 2021. Direct Exploitation of Attention Weights for Translation Quality Estimation. In Proceedings of the Sixth Conference on Machine Translation, pages 955–960, Online. Association for Computational Linguistics.
- Cite (Informal):
- Direct Exploitation of Attention Weights for Translation Quality Estimation (Yankovskaya & Fishel, WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.101.pdf
Export citation
@inproceedings{yankovskaya-fishel-2021-direct,
title = "Direct Exploitation of Attention Weights for Translation Quality Estimation",
author = "Yankovskaya, Lisa and
Fishel, Mark",
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-1.101/",
pages = "955--960",
abstract = "The paper presents our submission to the WMT2021 Shared Task on Quality Estimation (QE). We participate in sentence-level predictions of human judgments and post-editing effort. We propose a glass-box approach based on attention weights extracted from machine translation systems. In contrast to the previous works, we directly explore attention weight matrices without replacing them with general metrics (like entropy). We show that some of our models can be trained with a small amount of a high-cost labelled data. In the absence of training data our approach still demonstrates a moderate linear correlation, when trained with synthetic data."
}
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%0 Conference Proceedings %T Direct Exploitation of Attention Weights for Translation Quality Estimation %A Yankovskaya, Lisa %A Fishel, Mark %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 yankovskaya-fishel-2021-direct %X The paper presents our submission to the WMT2021 Shared Task on Quality Estimation (QE). We participate in sentence-level predictions of human judgments and post-editing effort. We propose a glass-box approach based on attention weights extracted from machine translation systems. In contrast to the previous works, we directly explore attention weight matrices without replacing them with general metrics (like entropy). We show that some of our models can be trained with a small amount of a high-cost labelled data. In the absence of training data our approach still demonstrates a moderate linear correlation, when trained with synthetic data. %U https://aclanthology.org/2021.wmt-1.101/ %P 955-960
Markdown (Informal)
[Direct Exploitation of Attention Weights for Translation Quality Estimation](https://aclanthology.org/2021.wmt-1.101/) (Yankovskaya & Fishel, WMT 2021)
- Direct Exploitation of Attention Weights for Translation Quality Estimation (Yankovskaya & Fishel, WMT 2021)
ACL
- Lisa Yankovskaya and Mark Fishel. 2021. Direct Exploitation of Attention Weights for Translation Quality Estimation. In Proceedings of the Sixth Conference on Machine Translation, pages 955–960, Online. Association for Computational Linguistics.