Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation

Diptesh Kanojia, Marina Fomicheva, Tharindu Ranasinghe, Frédéric Blain, Constantin Orăsan, Lucia Specia


Abstract
Current Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation outputs that can contain important meaning errors, thus undermining their reliability in practice. Quality Estimation (QE) is the task of automatically assessing the performance of MT systems at test time. Thus, in order to be useful, QE systems should be able to detect such errors. However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements. In this work, we bridge this gap by proposing a general methodology for adversarial testing of QE for MT. First, we show that despite a high correlation with human judgements achieved by the recent SOTA, certain types of meaning errors are still problematic for QE to detect. Second, we show that on average, the ability of a given model to discriminate between meaning-preserving and meaning-altering perturbations is predictive of its overall performance, thus potentially allowing for comparing QE systems without relying on manual quality annotation.
Anthology ID:
2021.wmt-1.67
Volume:
Proceedings of the Sixth Conference on Machine Translation
Month:
November
Year:
2021
Address:
Online
Venues:
EMNLP | WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
625–638
Language:
URL:
https://aclanthology.org/2021.wmt-1.67
DOI:
Bibkey:
Cite (ACL):
Diptesh Kanojia, Marina Fomicheva, Tharindu Ranasinghe, Frédéric Blain, Constantin Orăsan, and Lucia Specia. 2021. Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation. In Proceedings of the Sixth Conference on Machine Translation, pages 625–638, Online. Association for Computational Linguistics.
Cite (Informal):
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation (Kanojia et al., WMT 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wmt-1.67.pdf
Code
 dipteshkanojia/qe-evaluation
Data
WMT 2020