BLEU might be Guilty but References are not Innocent

Markus Freitag, David Grangier, Isaac Caswell


Abstract
The quality of automatic metrics for machine translation has been increasingly called into question, especially for high-quality systems. This paper demonstrates that, while choice of metric is important, the nature of the references is also critical. We study different methods to collect references and compare their value in automated evaluation by reporting correlation with human evaluation for a variety of systems and metrics. Motivated by the finding that typical references exhibit poor diversity, concentrating around translationese language, we develop a paraphrasing task for linguists to perform on existing reference translations, which counteracts this bias. Our method yields higher correlation with human judgment not only for the submissions of WMT 2019 English to German, but also for Back-translation and APE augmented MT output, which have been shown to have low correlation with automatic metrics using standard references. We demonstrate that our methodology improves correlation with all modern evaluation metrics we look at, including embedding-based methods.To complete this picture, we reveal that multi-reference BLEU does not improve the correlation for high quality output, and present an alternative multi-reference formulation that is more effective.
Anthology ID:
2020.emnlp-main.5
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
61–71
Language:
URL:
https://aclanthology.org/2020.emnlp-main.5
DOI:
10.18653/v1/2020.emnlp-main.5
Bibkey:
Cite (ACL):
Markus Freitag, David Grangier, and Isaac Caswell. 2020. BLEU might be Guilty but References are not Innocent. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 61–71, Online. Association for Computational Linguistics.
Cite (Informal):
BLEU might be Guilty but References are not Innocent (Freitag et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.5.pdf
Video:
 https://slideslive.com/38938647
Code
 google/wmt19-paraphrased-references +  additional community code