Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task

Brian Tubay, Marta R. Costa-jussà


Abstract
The Transformer architecture has become the state-of-the-art in Machine Translation. This model, which relies on attention-based mechanisms, has outperformed previous neural machine translation architectures in several tasks. In this system description paper, we report details of training neural machine translation with multi-source Romance languages with the Transformer model and in the evaluation frame of the biomedical WMT 2018 task. Using multi-source languages from the same family allows improvements of over 6 BLEU points.
Anthology ID:
W18-6449
Volume:
Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Month:
October
Year:
2018
Address:
Belgium, Brussels
Venues:
EMNLP | WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
667–670
Language:
URL:
https://www.aclweb.org/anthology/W18-6449
DOI:
10.18653/v1/W18-6449
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6449.pdf