@inproceedings{tubay-costa-jussa-2018-neural,
title = "Neural Machine Translation with the Transformer and Multi-Source {R}omance Languages for the Biomedical {WMT} 2018 task",
author = "Tubay, Brian and
Costa-juss{\`a}, Marta R.",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6449",
doi = "10.18653/v1/W18-6449",
pages = "667--670",
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.",
}
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%0 Conference Proceedings
%T Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task
%A Tubay, Brian
%A Costa-jussà, Marta R.
%Y Bojar, Ondřej
%Y Chatterjee, Rajen
%Y Federmann, Christian
%Y Fishel, Mark
%Y Graham, Yvette
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Specia, Lucia
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 October
%I Association for Computational Linguistics
%C Belgium, Brussels
%F tubay-costa-jussa-2018-neural
%X 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.
%R 10.18653/v1/W18-6449
%U https://aclanthology.org/W18-6449
%U https://doi.org/10.18653/v1/W18-6449
%P 667-670
Markdown (Informal)
[Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task](https://aclanthology.org/W18-6449) (Tubay & Costa-jussà, WMT 2018)
ACL