Regressive Ensemble for Machine Translation Quality Evaluation

Michal Stefanik, Vít Novotný, Petr Sojka


Abstract
This work introduces a simple regressive ensemble for evaluating machine translation quality based on a set of novel and established metrics. We evaluate the ensemble using a correlation to expert-based MQM scores of the WMT 2021 Metrics workshop. In both monolingual and zero-shot cross-lingual settings, we show a significant performance improvement over single metrics. In the cross-lingual settings, we also demonstrate that an ensemble approach is well-applicable to unseen languages. Furthermore, we identify a strong reference-free baseline that consistently outperforms the commonly-used BLEU and METEOR measures and significantly improves our ensemble’s performance.
Anthology ID:
2021.wmt-1.112
Volume:
Proceedings of the Sixth Conference on Machine Translation
Month:
November
Year:
2021
Address:
Online
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1041–1048
Language:
URL:
https://aclanthology.org/2021.wmt-1.112
DOI:
Bibkey:
Cite (ACL):
Michal Stefanik, Vít Novotný, and Petr Sojka. 2021. Regressive Ensemble for Machine Translation Quality Evaluation. In Proceedings of the Sixth Conference on Machine Translation, pages 1041–1048, Online. Association for Computational Linguistics.
Cite (Informal):
Regressive Ensemble for Machine Translation Quality Evaluation (Stefanik et al., WMT 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wmt-1.112.pdf
Code
 mir-mu/regemt
Data
MLQE-PE