TOWER-V2: Unbabel-IST 2024 Submission for the General MT Shared Task
Pombal, J.
; Rei , R.
;
Guerreiro, N.
; Alves, J.
;
Martins, P. H.
;
Fernandes, P.
; Wu, HW
; Vaz, T.
;
Alves, D. A.
; Farajian, M.
;
Agrawal, S. A.
;
Farinhas, A. F.
; de Souza, J. G.
;
Martins, A.
TOWER-V2: Unbabel-IST 2024 Submission for the General MT Shared Task, Proc Empirical Methods in Language Processing - EMNLP, Miami, United States, Vol. , pp. - , November, 2024.
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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-SONNET-3.5, and DEEPL even at a smaller 7B scale.
