@inproceedings{rei-etal-2023-scaling,
title = "Scaling up {C}omet{K}iwi: Unbabel-{IST} 2023 Submission for the Quality Estimation Shared Task",
author = "Rei, Ricardo and
Guerreiro, Nuno M. and
Pombal, Jos{\~A}{\copyright} and
van Stigt, Daan and
Treviso, Marcos and
Coheur, Luisa and
C. de Souza, Jos{\'e} G. and
Martins, Andr{\'e}",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.73",
doi = "10.18653/v1/2023.wmt-1.73",
pages = "841--848",
abstract = "We present the joint contribution of Unbabel and Instituto Superior T{\'e}cnico to the WMT 2023 Shared Task on Quality Estimation (QE). Our team participated on all tasks: Sentence- and Word-level Quality Prediction and Fine-grained error span detection. For all tasks we build on the CometKiwi model (rei et al. 2022). Our multilingual approaches are ranked first for all tasks, reaching state-of-the-art performance for quality estimation at word-, span- and sentence-level granularity. Compared to the previous state-of-the-art, CometKiwi, we show large improvements in correlation with human judgements (up to 10 Spearman points) and surpassing the second-best multilingual submission with up to 3.8 absolute points.",
}
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<abstract>We present the joint contribution of Unbabel and Instituto Superior Técnico to the WMT 2023 Shared Task on Quality Estimation (QE). Our team participated on all tasks: Sentence- and Word-level Quality Prediction and Fine-grained error span detection. For all tasks we build on the CometKiwi model (rei et al. 2022). Our multilingual approaches are ranked first for all tasks, reaching state-of-the-art performance for quality estimation at word-, span- and sentence-level granularity. Compared to the previous state-of-the-art, CometKiwi, we show large improvements in correlation with human judgements (up to 10 Spearman points) and surpassing the second-best multilingual submission with up to 3.8 absolute points.</abstract>
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%0 Conference Proceedings
%T Scaling up CometKiwi: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task
%A Rei, Ricardo
%A Guerreiro, Nuno M.
%A Pombal, JosÃ\copyright
%A van Stigt, Daan
%A Treviso, Marcos
%A Coheur, Luisa
%A C. de Souza, José G.
%A Martins, André
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F rei-etal-2023-scaling
%X We present the joint contribution of Unbabel and Instituto Superior Técnico to the WMT 2023 Shared Task on Quality Estimation (QE). Our team participated on all tasks: Sentence- and Word-level Quality Prediction and Fine-grained error span detection. For all tasks we build on the CometKiwi model (rei et al. 2022). Our multilingual approaches are ranked first for all tasks, reaching state-of-the-art performance for quality estimation at word-, span- and sentence-level granularity. Compared to the previous state-of-the-art, CometKiwi, we show large improvements in correlation with human judgements (up to 10 Spearman points) and surpassing the second-best multilingual submission with up to 3.8 absolute points.
%R 10.18653/v1/2023.wmt-1.73
%U https://aclanthology.org/2023.wmt-1.73
%U https://doi.org/10.18653/v1/2023.wmt-1.73
%P 841-848
Markdown (Informal)
[Scaling up CometKiwi: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task](https://aclanthology.org/2023.wmt-1.73) (Rei et al., WMT 2023)
ACL