The AFRL WMT19 Systems: Old Favorites and New Tricks

Jeremy Gwinnup, Grant Erdmann, Tim Anderson


Abstract
This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT19 evaluation campaign. This year, we refine our approach to training popular neural machine translation toolkits, experiment with a new domain adaptation technique and again measure improvements in performance on the Russian–English language pair.
Anthology ID:
W19-5318
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:
203–208
Language:
URL:
https://aclanthology.org/W19-5318
DOI:
10.18653/v1/W19-5318
Bibkey:
Cite (ACL):
Jeremy Gwinnup, Grant Erdmann, and Tim Anderson. 2019. The AFRL WMT19 Systems: Old Favorites and New Tricks. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 203–208, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
The AFRL WMT19 Systems: Old Favorites and New Tricks (Gwinnup et al., WMT 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5318.pdf