Findings of the WMT 2022 Shared Task on Efficient Translation
Kenneth Heafield, Biao Zhang, Graeme Nail, Jelmer Van Der Linde, Nikolay Bogoychev
Abstract
The machine translation efficiency task challenges participants to make their systems faster and smaller with minimal impact on translation quality. How much quality to sacrifice for efficiency depends upon the application, so participants were encouraged to make multiple submissions covering the space of trade-offs. In total, there were 76 submissions from 5 teams. The task covers GPU, single-core CPU, and multi-core CPU hardware tracks as well as batched throughput or single-sentence latency conditions. Submissions showed hundreds of millions of words can be translated for a dollar, average latency is 3.5–25 ms, and models fit in 7.5–900 MB.- Anthology ID:
- 2022.wmt-1.4
- 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:
- 100–108
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.4
- DOI:
- Bibkey:
- Cite (ACL):
- Kenneth Heafield, Biao Zhang, Graeme Nail, Jelmer Van Der Linde, and Nikolay Bogoychev. 2022. Findings of the WMT 2022 Shared Task on Efficient Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 100–108, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Findings of the WMT 2022 Shared Task on Efficient Translation (Heafield et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.4.pdf
Export citation
@inproceedings{heafield-etal-2022-findings,
title = "Findings of the {WMT} 2022 Shared Task on Efficient Translation",
author = "Heafield, Kenneth and
Zhang, Biao and
Nail, Graeme and
Van Der Linde, Jelmer and
Bogoychev, Nikolay",
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-1.4",
pages = "100--108",
abstract = "The machine translation efficiency task challenges participants to make their systems faster and smaller with minimal impact on translation quality. How much quality to sacrifice for efficiency depends upon the application, so participants were encouraged to make multiple submissions covering the space of trade-offs. In total, there were 76 submissions from 5 teams. The task covers GPU, single-core CPU, and multi-core CPU hardware tracks as well as batched throughput or single-sentence latency conditions. Submissions showed hundreds of millions of words can be translated for a dollar, average latency is 3.5{--}25 ms, and models fit in 7.5{--}900 MB.",
}
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%0 Conference Proceedings %T Findings of the WMT 2022 Shared Task on Efficient Translation %A Heafield, Kenneth %A Zhang, Biao %A Nail, Graeme %A Van Der Linde, Jelmer %A Bogoychev, Nikolay %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 heafield-etal-2022-findings %X The machine translation efficiency task challenges participants to make their systems faster and smaller with minimal impact on translation quality. How much quality to sacrifice for efficiency depends upon the application, so participants were encouraged to make multiple submissions covering the space of trade-offs. In total, there were 76 submissions from 5 teams. The task covers GPU, single-core CPU, and multi-core CPU hardware tracks as well as batched throughput or single-sentence latency conditions. Submissions showed hundreds of millions of words can be translated for a dollar, average latency is 3.5–25 ms, and models fit in 7.5–900 MB. %U https://aclanthology.org/2022.wmt-1.4 %P 100-108
Markdown (Informal)
[Findings of the WMT 2022 Shared Task on Efficient Translation](https://aclanthology.org/2022.wmt-1.4) (Heafield et al., WMT 2022)
- Findings of the WMT 2022 Shared Task on Efficient Translation (Heafield et al., WMT 2022)
ACL
- Kenneth Heafield, Biao Zhang, Graeme Nail, Jelmer Van Der Linde, and Nikolay Bogoychev. 2022. Findings of the WMT 2022 Shared Task on Efficient Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 100–108, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.