Taking MT evaluation metrics to extremes: beyond correlation with human judgments
File(s)
Published version
Author(s)
Fomicheva, Marina
Specia, Lucia
Type
Journal Article
Abstract
Automatic Machine Translation (MT) evaluation is an active field of research, with a handful of new metrics devised every year. Evaluation metrics are generally benchmarked against manual assessment of translation quality, with performance measured in terms of overall correlation with human scores. Much work has been dedicated to the improvement of evaluation metrics to achieve a higher correlation with human judgments. However, little insight has been provided regarding the weaknesses and strengths of existing approaches and their behavior in different settings. In this work we conduct a broad meta-evaluation study of the performance of a wide range of evaluation metrics focusing on three major aspects. First, we analyze the performance of the metrics when faced with different levels of translation quality, proposing a local dependency measure as an alternative to the standard, global correlation coefficient. We show that metric performance varies significantly across different levels of MT quality: Metrics perform poorly when faced with low-quality translations and are not able to capture nuanced quality distinctions. Interestingly, we show that evaluating low-quality translations is also more challenging for humans. Second, we show that metrics are more reliable when evaluating neural MT than the traditional statistical MT systems. Finally, we show that the difference in the evaluation accuracy for different metrics is maintained even if the gold standard scores are based on different criteria.
Date Issued
2019-09-01
Date Acceptance
2019-06-12
Citation
Computational Linguistics, 2019, 45 (3), pp.515-558
ISSN
0891-2017
Publisher
MIT Press
Start Page
515
End Page
558
Journal / Book Title
Computational Linguistics
Volume
45
Issue
3
Copyright Statement
© 2019 Association for Computational Linguistics
Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0) license
Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0) license
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000489035700004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Social Sciences
Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Linguistics
Language & Linguistics
Computer Science
LOCAL GAUSSIAN CORRELATION
INTERDEPENDENCE
DEPENDENCE
CONTAGION
Publication Status
Published
Date Publish Online
2019-09