Abstract
This paper describes anonymous submission to the WMT 2022 Quality Estimation shared task. We participate in Task 1: Quality Prediction for both sentence and word-level quality prediction tasks. Our system is a multilingual and multi-task model, whereby a single system can infer both sentence and word-level quality on multiple language pairs. Our system’s architecture consists of Pretrained Language Model (PLM) and task layers, and is jointly optimized for both sentence and word-level quality prediction tasks using multilingual dataset. We propose novel auxiliary tasks for training and explore diverse sources of additional data to demonstrate further improvements on performance. Through ablation study, we examine the effectiveness of proposed components and find optimal configurations to train our submission systems under each language pair and task settings. Finally, submission systems are trained and inferenced using K-folds ensemble. Our systems greatly outperform task organizer’s baseline and achieve comparable performance against other participants’ submissions in both sentence and word-level quality prediction tasks.- Anthology ID:
- 2022.wmt-1.59
- 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:
- 627–633
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.59
- DOI:
- Bibkey:
- Cite (ACL):
- Seunghyun Lim and Jeonghyeok Park. 2022. Papago’s Submission to the WMT22 Quality Estimation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 627–633, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Papago’s Submission to the WMT22 Quality Estimation Shared Task (Lim & Park, WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.59.pdf
Export citation
@inproceedings{lim-park-2022-papagos,
title = "Papago{'}s Submission to the {WMT}22 Quality Estimation Shared Task",
author = "Lim, Seunghyun and
Park, Jeonghyeok",
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-1.59",
pages = "627--633",
abstract = "This paper describes anonymous submission to the WMT 2022 Quality Estimation shared task. We participate in Task 1: Quality Prediction for both sentence and word-level quality prediction tasks. Our system is a multilingual and multi-task model, whereby a single system can infer both sentence and word-level quality on multiple language pairs. Our system{'}s architecture consists of Pretrained Language Model (PLM) and task layers, and is jointly optimized for both sentence and word-level quality prediction tasks using multilingual dataset. We propose novel auxiliary tasks for training and explore diverse sources of additional data to demonstrate further improvements on performance. Through ablation study, we examine the effectiveness of proposed components and find optimal configurations to train our submission systems under each language pair and task settings. Finally, submission systems are trained and inferenced using K-folds ensemble. Our systems greatly outperform task organizer{'}s baseline and achieve comparable performance against other participants{'} submissions in both sentence and word-level quality prediction tasks.",
}
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%0 Conference Proceedings %T Papago’s Submission to the WMT22 Quality Estimation Shared Task %A Lim, Seunghyun %A Park, Jeonghyeok %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 lim-park-2022-papagos %X This paper describes anonymous submission to the WMT 2022 Quality Estimation shared task. We participate in Task 1: Quality Prediction for both sentence and word-level quality prediction tasks. Our system is a multilingual and multi-task model, whereby a single system can infer both sentence and word-level quality on multiple language pairs. Our system’s architecture consists of Pretrained Language Model (PLM) and task layers, and is jointly optimized for both sentence and word-level quality prediction tasks using multilingual dataset. We propose novel auxiliary tasks for training and explore diverse sources of additional data to demonstrate further improvements on performance. Through ablation study, we examine the effectiveness of proposed components and find optimal configurations to train our submission systems under each language pair and task settings. Finally, submission systems are trained and inferenced using K-folds ensemble. Our systems greatly outperform task organizer’s baseline and achieve comparable performance against other participants’ submissions in both sentence and word-level quality prediction tasks. %U https://aclanthology.org/2022.wmt-1.59 %P 627-633
Markdown (Informal)
[Papago’s Submission to the WMT22 Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.59) (Lim & Park, WMT 2022)
- Papago’s Submission to the WMT22 Quality Estimation Shared Task (Lim & Park, WMT 2022)
ACL
- Seunghyun Lim and Jeonghyeok Park. 2022. Papago’s Submission to the WMT22 Quality Estimation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 627–633, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.