Abstract
This paper presents our submission to the WMT 2022 quality estimation shared task and more specifically to the quality prediction sentence-level direct assessment (DA) subtask. We build a multilingual system based on the predictor–estimator architecture by using the XLM-RoBERTa transformer for feature extraction and a regression head on top of the final model to estimate the z-standardized DA labels. Furthermore, we use pretrained models to extract useful knowledge that reflect various criteria of quality assessment and demonstrate good correlation with human judgements. We optimize the performance of our model by incorporating this information as additional external features in the input data and by applying Monte Carlo dropout during both training and inference.- Anthology ID:
- 2022.wmt-1.62
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 653–660
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.62
- DOI:
- Bibkey:
- Cite (ACL):
- Eirini Zafeiridou and Sokratis Sofianopoulos. 2022. Welocalize-ARC/NKUA’s Submission to the WMT 2022 Quality Estimation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 653–660, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Welocalize-ARC/NKUA’s Submission to the WMT 2022 Quality Estimation Shared Task (Zafeiridou & Sofianopoulos, WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.62.pdf
Export citation
@inproceedings{zafeiridou-sofianopoulos-2022-welocalize,
title = "Welocalize-{ARC}/{NKUA}{'}s Submission to the {WMT} 2022 Quality Estimation Shared Task",
author = "Zafeiridou, Eirini and
Sofianopoulos, Sokratis",
editor = {Koehn, Philipp and
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
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.62",
pages = "653--660",
abstract = "This paper presents our submission to the WMT 2022 quality estimation shared task and more specifically to the quality prediction sentence-level direct assessment (DA) subtask. We build a multilingual system based on the predictor{--}estimator architecture by using the XLM-RoBERTa transformer for feature extraction and a regression head on top of the final model to estimate the $z$-standardized DA labels. Furthermore, we use pretrained models to extract useful knowledge that reflect various criteria of quality assessment and demonstrate good correlation with human judgements. We optimize the performance of our model by incorporating this information as additional external features in the input data and by applying Monte Carlo dropout during both training and inference.",
}
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%0 Conference Proceedings %T Welocalize-ARC/NKUA’s Submission to the WMT 2022 Quality Estimation Shared Task %A Zafeiridou, Eirini %A Sofianopoulos, Sokratis %Y Koehn, Philipp %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 Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F zafeiridou-sofianopoulos-2022-welocalize %X This paper presents our submission to the WMT 2022 quality estimation shared task and more specifically to the quality prediction sentence-level direct assessment (DA) subtask. We build a multilingual system based on the predictor–estimator architecture by using the XLM-RoBERTa transformer for feature extraction and a regression head on top of the final model to estimate the z-standardized DA labels. Furthermore, we use pretrained models to extract useful knowledge that reflect various criteria of quality assessment and demonstrate good correlation with human judgements. We optimize the performance of our model by incorporating this information as additional external features in the input data and by applying Monte Carlo dropout during both training and inference. %U https://aclanthology.org/2022.wmt-1.62 %P 653-660
Markdown (Informal)
[Welocalize-ARC/NKUA’s Submission to the WMT 2022 Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.62) (Zafeiridou & Sofianopoulos, WMT 2022)
- Welocalize-ARC/NKUA’s Submission to the WMT 2022 Quality Estimation Shared Task (Zafeiridou & Sofianopoulos, WMT 2022)
ACL
- Eirini Zafeiridou and Sokratis Sofianopoulos. 2022. Welocalize-ARC/NKUA’s Submission to the WMT 2022 Quality Estimation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 653–660, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.