EED: Extended Edit Distance Measure for Machine Translation

Peter Stanchev, Weiyue Wang, Hermann Ney


Abstract
Over the years a number of machine translation metrics have been developed in order to evaluate the accuracy and quality of machine-generated translations. Metrics such as BLEU and TER have been used for decades. However, with the rapid progress of machine translation systems, the need for better metrics is growing. This paper proposes an extension of the edit distance, which achieves better human correlation, whilst remaining fast, flexible and easy to understand.
Anthology ID:
W19-5359
Volume:
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
514–520
Language:
URL:
https://aclanthology.org/W19-5359
DOI:
10.18653/v1/W19-5359
Bibkey:
Cite (ACL):
Peter Stanchev, Weiyue Wang, and Hermann Ney. 2019. EED: Extended Edit Distance Measure for Machine Translation. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 514–520, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
EED: Extended Edit Distance Measure for Machine Translation (Stanchev et al., WMT 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5359.pdf