Zhuo, Terry Yue;
Xu, Qiongkai;
He, Xuanli;
Cohn, Trevor;
(2023)
Rethinking Round-Trip Translation for Machine Translation Evaluation.
In: Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki, (eds.)
Proceedings of the Annual Meeting of the Association for Computational Linguistics: ACL 2023.
(pp. pp. 319-337).
Association for Computational Linguistics: Toronto, Canada.
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Abstract
Automatic evaluation methods for translation often require model training, and thus the availability of parallel corpora limits their applicability to low-resource settings. Round-trip translation is a potential workaround, which can reframe bilingual evaluation into a much simpler monolingual task. Early results from the era of statistical machine translation (SMT) raised fundamental concerns about the utility of this approach, based on poor correlation with human translation quality judgments. In this paper, we revisit this technique with modern neural translation (NMT) and show that round-trip translation does allow for accurate automatic evaluation without the need for reference translations. These opposite findings can be explained through the copy mechanism in SMT that is absent in NMT. We demonstrate that round-trip translation benefits multiple machine translation evaluation tasks: i) predicting forward translation scores; ii) improving the performance of a quality estimation model; and iii) identifying adversarial competitors in shared tasks via cross-system verification.
Type: | Proceedings paper |
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Title: | Rethinking Round-Trip Translation for Machine Translation Evaluation |
Event: | Findings of the Association for Computational Linguistics: ACL 2023 |
ISBN-13: | 9781959429623 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.18653/v1/2023.findings-acl.22 |
Publisher version: | https://doi.org/10.18653/v1/2023.findings-acl.22 |
Language: | English |
Additional information: | © The Author(s), 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10188437 |
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