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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W19-5318.pdf