Edinburgh’s Submission to the WMT 2022 Efficiency Task
Nikolay Bogoychev, Maximiliana Behnke, Jelmer Van Der Linde, Graeme Nail, Kenneth Heafield, Biao Zhang, Sidharth Kashyap
Abstract
We participated in all tracks of the WMT 2022 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions. Our submissions explores a number of several efficiency strategies: knowledge distillation, a simpler simple recurrent unit (SSRU) decoder with one or two layers, shortlisting, deep encoder, shallow decoder, pruning and bidirectional decoder. For the CPU track, we used quantized 8-bit models. For the GPU track, we used FP16 quantisation. We explored various pruning strategies and combination of one or more of the above methods.- Anthology ID:
- 2022.wmt-1.63
- 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:
- 661–667
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.63
- DOI:
- Bibkey:
- Cite (ACL):
- Nikolay Bogoychev, Maximiliana Behnke, Jelmer Van Der Linde, Graeme Nail, Kenneth Heafield, Biao Zhang, and Sidharth Kashyap. 2022. Edinburgh’s Submission to the WMT 2022 Efficiency Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 661–667, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Edinburgh’s Submission to the WMT 2022 Efficiency Task (Bogoychev et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.63.pdf
Export citation
@inproceedings{bogoychev-etal-2022-edinburghs,
title = "{E}dinburgh{'}s Submission to the {WMT} 2022 Efficiency Task",
author = "Bogoychev, Nikolay and
Behnke, Maximiliana and
Van Der Linde, Jelmer and
Nail, Graeme and
Heafield, Kenneth and
Zhang, Biao and
Kashyap, Sidharth",
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-1.63",
pages = "661--667",
abstract = "We participated in all tracks of the WMT 2022 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions. Our submissions explores a number of several efficiency strategies: knowledge distillation, a simpler simple recurrent unit (SSRU) decoder with one or two layers, shortlisting, deep encoder, shallow decoder, pruning and bidirectional decoder. For the CPU track, we used quantized 8-bit models. For the GPU track, we used FP16 quantisation. We explored various pruning strategies and combination of one or more of the above methods.",
}
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%0 Conference Proceedings %T Edinburgh’s Submission to the WMT 2022 Efficiency Task %A Bogoychev, Nikolay %A Behnke, Maximiliana %A Van Der Linde, Jelmer %A Nail, Graeme %A Heafield, Kenneth %A Zhang, Biao %A Kashyap, Sidharth %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 bogoychev-etal-2022-edinburghs %X We participated in all tracks of the WMT 2022 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions. Our submissions explores a number of several efficiency strategies: knowledge distillation, a simpler simple recurrent unit (SSRU) decoder with one or two layers, shortlisting, deep encoder, shallow decoder, pruning and bidirectional decoder. For the CPU track, we used quantized 8-bit models. For the GPU track, we used FP16 quantisation. We explored various pruning strategies and combination of one or more of the above methods. %U https://aclanthology.org/2022.wmt-1.63 %P 661-667
Markdown (Informal)
[Edinburgh’s Submission to the WMT 2022 Efficiency Task](https://aclanthology.org/2022.wmt-1.63) (Bogoychev et al., WMT 2022)
- Edinburgh’s Submission to the WMT 2022 Efficiency Task (Bogoychev et al., WMT 2022)
ACL
- Nikolay Bogoychev, Maximiliana Behnke, Jelmer Van Der Linde, Graeme Nail, Kenneth Heafield, Biao Zhang, and Sidharth Kashyap. 2022. Edinburgh’s Submission to the WMT 2022 Efficiency Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 661–667, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.