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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.</abstract>
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%0 Conference Proceedings
%T PROMT Systems for WMT22 General Translation Task
%A Molchanov, Alexander
%A Kovalenko, Vladislav
%A Makhamalkina, Natalia
%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

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.