@inproceedings{blain-etal-2023-findings,
title = "Findings of the {WMT} 2023 Shared Task on Quality Estimation",
author = "Blain, Frederic and
Zerva, Chrysoula and
Rei, Ricardo and
Guerreiro, Nuno M. and
Kanojia, Diptesh and
C. de Souza, Jos{\'e} G. and
Silva, Beatriz and
Vaz, T{\^a}nia and
Jingxuan, Yan and
Azadi, Fatemeh and
Orasan, Constantin 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-1.52",
doi = "10.18653/v1/2023.wmt-1.52",
pages = "629--653",
abstract = "We report the results of the WMT 2023 shared task on Quality Estimation, in which the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels, without access to reference translations. This edition introduces a few novel aspects and extensions that aim to enable more fine-grained, and explainable quality estimation approaches. We introduce an updated quality annotation scheme using Multidimensional Quality Metrics to obtain sentence- and word-level quality scores for three language pairs. We also extend the provided data to new language pairs: we specifically target low-resource languages and provide training, development and test data for English-Hindi, English-Tamil, English-Telegu and English-Gujarati as well as a zero-shot test-set for English-Farsi. Further, we introduce a novel fine-grained error prediction task aspiring to motivate research towards more detailed quality predictions.",
}
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<abstract>We report the results of the WMT 2023 shared task on Quality Estimation, in which the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels, without access to reference translations. This edition introduces a few novel aspects and extensions that aim to enable more fine-grained, and explainable quality estimation approaches. We introduce an updated quality annotation scheme using Multidimensional Quality Metrics to obtain sentence- and word-level quality scores for three language pairs. We also extend the provided data to new language pairs: we specifically target low-resource languages and provide training, development and test data for English-Hindi, English-Tamil, English-Telegu and English-Gujarati as well as a zero-shot test-set for English-Farsi. Further, we introduce a novel fine-grained error prediction task aspiring to motivate research towards more detailed quality predictions.</abstract>
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%0 Conference Proceedings
%T Findings of the WMT 2023 Shared Task on Quality Estimation
%A Blain, Frederic
%A Zerva, Chrysoula
%A Rei, Ricardo
%A Guerreiro, Nuno M.
%A Kanojia, Diptesh
%A C. de Souza, José G.
%A Silva, Beatriz
%A Vaz, Tânia
%A Jingxuan, Yan
%A Azadi, Fatemeh
%A Orasan, Constantin
%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 blain-etal-2023-findings
%X We report the results of the WMT 2023 shared task on Quality Estimation, in which the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels, without access to reference translations. This edition introduces a few novel aspects and extensions that aim to enable more fine-grained, and explainable quality estimation approaches. We introduce an updated quality annotation scheme using Multidimensional Quality Metrics to obtain sentence- and word-level quality scores for three language pairs. We also extend the provided data to new language pairs: we specifically target low-resource languages and provide training, development and test data for English-Hindi, English-Tamil, English-Telegu and English-Gujarati as well as a zero-shot test-set for English-Farsi. Further, we introduce a novel fine-grained error prediction task aspiring to motivate research towards more detailed quality predictions.
%R 10.18653/v1/2023.wmt-1.52
%U https://aclanthology.org/2023.wmt-1.52
%U https://doi.org/10.18653/v1/2023.wmt-1.52
%P 629-653
Markdown (Informal)
[Findings of the WMT 2023 Shared Task on Quality Estimation](https://aclanthology.org/2023.wmt-1.52) (Blain et al., WMT 2023)
ACL
- Frederic Blain, Chrysoula Zerva, Ricardo Rei, Nuno M. Guerreiro, Diptesh Kanojia, José G. C. de Souza, Beatriz Silva, Tânia Vaz, Yan Jingxuan, Fatemeh Azadi, Constantin Orasan, and André Martins. 2023. Findings of the WMT 2023 Shared Task on Quality Estimation. In Proceedings of the Eighth Conference on Machine Translation, pages 629–653, Singapore. Association for Computational Linguistics.