A Holistic Approach to Reference-Free Evaluation of Machine Translation

Hanming Wu, Wenjuan Han, Hui Di, Yufeng Chen, Jinan Xu


Abstract
Traditional machine translation evaluation relies on reference written by humans. While reference-free evaluation gets rid of the constraints of labor-intensive annotations, which can pivot easily to new domains and is more scalable. In this paper, we propose a reference-free evaluation approach that characterizes evaluation as two aspects: (1) fluency: how well the translated text conforms to normal human language usage; (2) faithfulness: how well the translated text reflects the source data. We further split the faithfulness into word-level and sentence-level. Extensive experiments spanning WMT18/19/21 Metrics segment-level daRR and MQM datasets demonstrate that our proposed reference-free approach, ReFreeEval, outperforms SOTA reference-fee metrics like YiSi-2.
Anthology ID:
2023.acl-short.55
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
623–636
Language:
URL:
https://aclanthology.org/2023.acl-short.55
DOI:
10.18653/v1/2023.acl-short.55
Bibkey:
Cite (ACL):
Hanming Wu, Wenjuan Han, Hui Di, Yufeng Chen, and Jinan Xu. 2023. A Holistic Approach to Reference-Free Evaluation of Machine Translation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 623–636, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
A Holistic Approach to Reference-Free Evaluation of Machine Translation (Wu et al., ACL 2023)
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PDF:
https://aclanthology.org/2023.acl-short.55.pdf
Video:
 https://aclanthology.org/2023.acl-short.55.mp4