Quality-Aware Decoding for Neural Machine Translation

Patrick Fernandes, António Farinhas, Ricardo Rei, José De Souza, Perez Ogayo, Graham Neubig, Andre Martins


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.
Anthology ID:
2022.naacl-main.100
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1396–1412
Language:
URL:
https://aclanthology.org/2022.naacl-main.100
DOI:
10.18653/v1/2022.naacl-main.100
Bibkey:
Cite (ACL):
Patrick Fernandes, António Farinhas, Ricardo Rei, José 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.
Cite (Informal):
Quality-Aware Decoding for Neural Machine Translation (Fernandes et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.100.pdf
Code
 deep-spin/qaware-decode