PROMT Systems for WMT22 General Translation Task

Alexander Molchanov; Vladislav Kovalenko; Natalia Makhamalkina

PROMT Systems for WMT22 General Translation Task

Alexander Molchanov, Vladislav Kovalenko, Natalia Makhamalkina

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Abstract

The PROMT systems are trained with the MarianNMT toolkit. All systems use the transformer-big configuration. We use BPE for text encoding, the vocabulary sizes vary from 24k to 32k for different language pairs. All systems are unconstrained. We use all data provided by the WMT organizers, all publicly available data and some private data. We participate in four directions: English-Russian, English-German and German-English, Ukrainian-English.

Anthology ID:: 2022.wmt-1.28
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:: 342–345
Language:
URL:: https://aclanthology.org/2022.wmt-1.28/
DOI:
Bibkey:
Cite (ACL):: Alexander Molchanov, Vladislav Kovalenko, and Natalia Makhamalkina. 2022. PROMT Systems for WMT22 General Translation Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 342–345, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):: PROMT Systems for WMT22 General Translation Task (Molchanov et al., WMT 2022)
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PDF:: https://aclanthology.org/2022.wmt-1.28.pdf

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@inproceedings{molchanov-etal-2022-promt,
    title = "{PROMT} Systems for {WMT}22 General Translation Task",
    author = "Molchanov, Alexander  and
      Kovalenko, Vladislav  and
      Makhamalkina, Natalia",
    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-1.28/",
    pages = "342--345",
    abstract = "The PROMT systems are trained with the MarianNMT toolkit. All systems use the transformer-big configuration. We use BPE for text encoding, the vocabulary sizes vary from 24k to 32k for different language pairs. All systems are unconstrained. We use all data provided by the WMT organizers, all publicly available data and some private data. We participate in four directions: English-Russian, English-German and German-English, Ukrainian-English."
}

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%0 Conference Proceedings
%T PROMT Systems for WMT22 General Translation Task
%A Molchanov, Alexander
%A Kovalenko, Vladislav
%A Makhamalkina, Natalia
%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 molchanov-etal-2022-promt
%X The PROMT systems are trained with the MarianNMT toolkit. All systems use the transformer-big configuration. We use BPE for text encoding, the vocabulary sizes vary from 24k to 32k for different language pairs. All systems are unconstrained. We use all data provided by the WMT organizers, all publicly available data and some private data. We participate in four directions: English-Russian, English-German and German-English, Ukrainian-English.
%U https://aclanthology.org/2022.wmt-1.28/
%P 342-345

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Markdown (Informal)

[PROMT Systems for WMT22 General Translation Task](https://aclanthology.org/2022.wmt-1.28/) (Molchanov et al., WMT 2022)

PROMT Systems for WMT22 General Translation Task (Molchanov et al., WMT 2022)

ACL

Alexander Molchanov, Vladislav Kovalenko, and Natalia Makhamalkina. 2022. PROMT Systems for WMT22 General Translation Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 342–345, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.