Multi-Source Transformer for Kazakh-Russian-English Neural Machine Translation

Patrick Littell, Chi-kiu Lo, Samuel Larkin, Darlene Stewart


Abstract
We describe the neural machine translation (NMT) system developed at the National Research Council of Canada (NRC) for the Kazakh-English news translation task of the Fourth Conference on Machine Translation (WMT19). Our submission is a multi-source NMT taking both the original Kazakh sentence and its Russian translation as input for translating into English.
Anthology ID:
W19-5326
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:
267–274
Language:
URL:
https://aclanthology.org/W19-5326
DOI:
10.18653/v1/W19-5326
Bibkey:
Cite (ACL):
Patrick Littell, Chi-kiu Lo, Samuel Larkin, and Darlene Stewart. 2019. Multi-Source Transformer for Kazakh-Russian-English Neural Machine Translation. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 267–274, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Multi-Source Transformer for Kazakh-Russian-English Neural Machine Translation (Littell et al., WMT 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5326.pdf