@inproceedings{fernandes-etal-2022-quality,
title = "Quality-Aware Decoding for Neural Machine Translation",
author = "Fernandes, Patrick and
Farinhas, Ant{\'o}nio and
Rei, Ricardo and
C. de Souza, Jos{\'e} G. and
Ogayo, Perez and
Neubig, Graham and
Martins, Andre",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.100",
doi = "10.18653/v1/2022.naacl-main.100",
pages = "1396--1412",
abstract = "Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search. In this paper, we bring together these two lines of research and propose \textit{quality-aware decoding} for NMT, by leveraging recent breakthroughs in reference-free and reference-based MT evaluation through various inference methods like $N$-best reranking and minimum Bayes risk decoding. We perform an extensive comparison of various possible candidate generation and ranking methods across four datasets and two model classes and find that quality-aware decoding consistently outperforms MAP-based decoding according both to state-of-the-art automatic metrics (COMET and BLEURT) and to human assessments.",
}
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<abstract>Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search. In this paper, we bring together these two lines of research and propose quality-aware decoding for NMT, by leveraging recent breakthroughs in reference-free and reference-based MT evaluation through various inference methods like N-best reranking and minimum Bayes risk decoding. We perform an extensive comparison of various possible candidate generation and ranking methods across four datasets and two model classes and find that quality-aware decoding consistently outperforms MAP-based decoding according both to state-of-the-art automatic metrics (COMET and BLEURT) and to human assessments.</abstract>
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%0 Conference Proceedings
%T Quality-Aware Decoding for Neural Machine Translation
%A Fernandes, Patrick
%A Farinhas, António
%A Rei, Ricardo
%A C. de Souza, José G.
%A Ogayo, Perez
%A Neubig, Graham
%A Martins, Andre
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F fernandes-etal-2022-quality
%X Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search. In this paper, we bring together these two lines of research and propose quality-aware decoding for NMT, by leveraging recent breakthroughs in reference-free and reference-based MT evaluation through various inference methods like N-best reranking and minimum Bayes risk decoding. We perform an extensive comparison of various possible candidate generation and ranking methods across four datasets and two model classes and find that quality-aware decoding consistently outperforms MAP-based decoding according both to state-of-the-art automatic metrics (COMET and BLEURT) and to human assessments.
%R 10.18653/v1/2022.naacl-main.100
%U https://aclanthology.org/2022.naacl-main.100
%U https://doi.org/10.18653/v1/2022.naacl-main.100
%P 1396-1412
Markdown (Informal)
[Quality-Aware Decoding for Neural Machine Translation](https://aclanthology.org/2022.naacl-main.100) (Fernandes et al., NAACL 2022)
ACL
- Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, and Andre Martins. 2022. Quality-Aware Decoding for Neural Machine Translation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1396–1412, Seattle, United States. Association for Computational Linguistics.