Uncertainty-Aware Machine Translation Evaluation

Taisiya Glushkova, Chrysoula Zerva, Ricardo Rei, André F. T. Martins


Abstract
Several neural-based metrics have been recently proposed to evaluate machine translation quality. However, all of them resort to point estimates, which provide limited information at segment level. This is made worse as they are trained on noisy, biased and scarce human judgements, often resulting in unreliable quality predictions. In this paper, we introduce uncertainty-aware MT evaluation and analyze the trustworthiness of the predicted quality. We combine the COMET framework with two uncertainty estimation methods, Monte Carlo dropout and deep ensembles, to obtain quality scores along with confidence intervals. We compare the performance of our uncertainty-aware MT evaluation methods across multiple language pairs from the QT21 dataset and the WMT20 metrics task, augmented with MQM annotations. We experiment with varying numbers of references and further discuss the usefulness of uncertainty-aware quality estimation (without references) to flag possibly critical translation mistakes.
Anthology ID:
2021.findings-emnlp.330
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3920–3938
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.330
DOI:
10.18653/v1/2021.findings-emnlp.330
Bibkey:
Cite (ACL):
Taisiya Glushkova, Chrysoula Zerva, Ricardo Rei, and André F. T. Martins. 2021. Uncertainty-Aware Machine Translation Evaluation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3920–3938, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Uncertainty-Aware Machine Translation Evaluation (Glushkova et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.330.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.330.mp4
Code
 Unbabel/COMET +  additional community code