@inproceedings{rei-etal-2024-tower,
title = "Tower v2: Unbabel-{IST} 2024 Submission for the General {MT} Shared Task",
author = "Rei, Ricardo and
Pombal, Jose and
Guerreiro, Nuno M. and
Alves, Jo{\~a}o and
Martins, Pedro Henrique and
Fernandes, Patrick and
Wu, Helena and
Vaz, Tania and
Alves, Duarte and
Farajian, Amin and
Agrawal, Sweta and
Farinhas, Antonio and
C. De Souza, Jos{\'e} G. and
Martins, Andr{\'e}",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Ninth Conference on Machine Translation",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wmt-1.12",
doi = "10.18653/v1/2024.wmt-1.12",
pages = "185--204",
abstract = "In this work, we present Tower v2, an improved iteration of the state-of-the-art open-weight Tower models, and the backbone of our submission to the WMT24 General Translation shared task. Tower v2 introduces key improvements including expanded language coverage, enhanced data quality, and increased model capacity up to 70B parameters. Our final submission combines these advancements with quality-aware decoding strategies, selecting translations based on multiple translation quality signals. The resulting system demonstrates significant improvement over previous versions, outperforming closed commercial systems like GPT-4o, Claude 3.5, and DeepL even at a smaller 7B scale.",
}
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<abstract>In this work, we present Tower v2, an improved iteration of the state-of-the-art open-weight Tower models, and the backbone of our submission to the WMT24 General Translation shared task. Tower v2 introduces key improvements including expanded language coverage, enhanced data quality, and increased model capacity up to 70B parameters. Our final submission combines these advancements with quality-aware decoding strategies, selecting translations based on multiple translation quality signals. The resulting system demonstrates significant improvement over previous versions, outperforming closed commercial systems like GPT-4o, Claude 3.5, and DeepL even at a smaller 7B scale.</abstract>
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%0 Conference Proceedings
%T Tower v2: Unbabel-IST 2024 Submission for the General MT Shared Task
%A Rei, Ricardo
%A Pombal, Jose
%A Guerreiro, Nuno M.
%A Alves, João
%A Martins, Pedro Henrique
%A Fernandes, Patrick
%A Wu, Helena
%A Vaz, Tania
%A Alves, Duarte
%A Farajian, Amin
%A Agrawal, Sweta
%A Farinhas, Antonio
%A C. De Souza, José G.
%A Martins, André
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F rei-etal-2024-tower
%X In this work, we present Tower v2, an improved iteration of the state-of-the-art open-weight Tower models, and the backbone of our submission to the WMT24 General Translation shared task. Tower v2 introduces key improvements including expanded language coverage, enhanced data quality, and increased model capacity up to 70B parameters. Our final submission combines these advancements with quality-aware decoding strategies, selecting translations based on multiple translation quality signals. The resulting system demonstrates significant improvement over previous versions, outperforming closed commercial systems like GPT-4o, Claude 3.5, and DeepL even at a smaller 7B scale.
%R 10.18653/v1/2024.wmt-1.12
%U https://aclanthology.org/2024.wmt-1.12
%U https://doi.org/10.18653/v1/2024.wmt-1.12
%P 185-204
Markdown (Informal)
[Tower v2: Unbabel-IST 2024 Submission for the General MT Shared Task](https://aclanthology.org/2024.wmt-1.12) (Rei et al., WMT 2024)
ACL
- Ricardo Rei, Jose Pombal, Nuno M. Guerreiro, João Alves, Pedro Henrique Martins, Patrick Fernandes, Helena Wu, Tania Vaz, Duarte Alves, Amin Farajian, Sweta Agrawal, Antonio Farinhas, José G. C. De Souza, and André Martins. 2024. Tower v2: Unbabel-IST 2024 Submission for the General MT Shared Task. In Proceedings of the Ninth Conference on Machine Translation, pages 185–204, Miami, Florida, USA. Association for Computational Linguistics.