@inproceedings{naskar-etal-2023-quality,
title = "Quality Estimation Using Minimum {B}ayes Risk",
author = "Naskar, Subhajit and
Deutsch, Daniel and
Freitag, Markus",
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-1.67",
doi = "10.18653/v1/2023.wmt-1.67",
pages = "806--811",
abstract = "This report describes the Minimum Bayes Risk Quality Estimation (MBR-QE) submission to the Workshop on Machine Translation{'}s 2023 Metrics Shared Task. MBR decoding with neural utility metrics like BLEURT is known to be effective in generating high quality machine translations. We use the underlying technique of MBR decoding and develop an MBR based reference-free quality estimation metric. Our method uses an evaluator machine translation system and a reference-based utility metric (specifically BLEURT and MetricX) to calculate a quality estimation score of a model. We report results related to comparing different MBR configurations and utility metrics.",
}
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<abstract>This report describes the Minimum Bayes Risk Quality Estimation (MBR-QE) submission to the Workshop on Machine Translation’s 2023 Metrics Shared Task. MBR decoding with neural utility metrics like BLEURT is known to be effective in generating high quality machine translations. We use the underlying technique of MBR decoding and develop an MBR based reference-free quality estimation metric. Our method uses an evaluator machine translation system and a reference-based utility metric (specifically BLEURT and MetricX) to calculate a quality estimation score of a model. We report results related to comparing different MBR configurations and utility metrics.</abstract>
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%0 Conference Proceedings
%T Quality Estimation Using Minimum Bayes Risk
%A Naskar, Subhajit
%A Deutsch, Daniel
%A Freitag, Markus
%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 naskar-etal-2023-quality
%X This report describes the Minimum Bayes Risk Quality Estimation (MBR-QE) submission to the Workshop on Machine Translation’s 2023 Metrics Shared Task. MBR decoding with neural utility metrics like BLEURT is known to be effective in generating high quality machine translations. We use the underlying technique of MBR decoding and develop an MBR based reference-free quality estimation metric. Our method uses an evaluator machine translation system and a reference-based utility metric (specifically BLEURT and MetricX) to calculate a quality estimation score of a model. We report results related to comparing different MBR configurations and utility metrics.
%R 10.18653/v1/2023.wmt-1.67
%U https://aclanthology.org/2023.wmt-1.67
%U https://doi.org/10.18653/v1/2023.wmt-1.67
%P 806-811
Markdown (Informal)
[Quality Estimation Using Minimum Bayes Risk](https://aclanthology.org/2023.wmt-1.67) (Naskar et al., WMT 2023)
ACL
- Subhajit Naskar, Daniel Deutsch, and Markus Freitag. 2023. Quality Estimation Using Minimum Bayes Risk. In Proceedings of the Eighth Conference on Machine Translation, pages 806–811, Singapore. Association for Computational Linguistics.