UniTE: Unified Translation Evaluation

Yu Wan, Dayiheng Liu, Baosong Yang, Haibo Zhang, Boxing Chen, Derek Wong, Lidia Chao


Abstract
Translation quality evaluation plays a crucial role in machine translation. According to the input format, it is mainly separated into three tasks, i.e., reference-only, source-only and source-reference-combined. Recent methods, despite their promising results, are specifically designed and optimized on one of them. This limits the convenience of these methods, and overlooks the commonalities among tasks. In this paper, we propose , which is the first unified framework engaged with abilities to handle all three evaluation tasks. Concretely, we propose monotonic regional attention to control the interaction among input segments, and unified pretraining to better adapt multi-task training. We testify our framework on WMT 2019 Metrics and WMT 2020 Quality Estimation benchmarks. Extensive analyses show that our single model can universally surpass various state-of-the-art or winner methods across tasks.Both source code and associated models are available at https://github.com/NLP2CT/UniTE.
Anthology ID:
2022.acl-long.558
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8117–8127
Language:
URL:
https://aclanthology.org/2022.acl-long.558
DOI:
10.18653/v1/2022.acl-long.558
Bibkey:
Cite (ACL):
Yu Wan, Dayiheng Liu, Baosong Yang, Haibo Zhang, Boxing Chen, Derek Wong, and Lidia Chao. 2022. UniTE: Unified Translation Evaluation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8117–8127, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
UniTE: Unified Translation Evaluation (Wan et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.558.pdf
Software:
 2022.acl-long.558.software.zip
Code
 nlp2ct/unite +  additional community code