@inproceedings{berard-etal-2020-multilingual,
title = "A Multilingual Neural Machine Translation Model for Biomedical Data",
author = "B{\'e}rard, Alexandre and
Kim, Zae Myung and
Nikoulina, Vassilina and
Park, Eunjeong Lucy and
Gall{\'e}, Matthias",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Conway, Michael and
de Bruijn, Berry and
Dredze, Mark and
Mihalcea, Rada and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-2.16",
doi = "10.18653/v1/2020.nlpcovid19-2.16",
abstract = "We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain. The model can translate from 5 languages (French, German, Italian, Korean and Spanish) into English. It is trained with large amounts of generic and biomedical data, using domain tags. Our benchmarks show that it performs near state-of-the-art both on news (generic domain) and biomedical test sets, and that it outperforms the existing publicly released models. We believe that this release will help the large-scale multilingual analysis of the digital content of the COVID-19 crisis and of its effects on society, economy, and healthcare policies. We also release a test set of biomedical text for Korean-English. It consists of 758 sentences from official guidelines and recent papers, all about COVID-19.",
}
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%0 Conference Proceedings
%T A Multilingual Neural Machine Translation Model for Biomedical Data
%A Bérard, Alexandre
%A Kim, Zae Myung
%A Nikoulina, Vassilina
%A Park, Eunjeong Lucy
%A Gallé, Matthias
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F berard-etal-2020-multilingual
%X We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain. The model can translate from 5 languages (French, German, Italian, Korean and Spanish) into English. It is trained with large amounts of generic and biomedical data, using domain tags. Our benchmarks show that it performs near state-of-the-art both on news (generic domain) and biomedical test sets, and that it outperforms the existing publicly released models. We believe that this release will help the large-scale multilingual analysis of the digital content of the COVID-19 crisis and of its effects on society, economy, and healthcare policies. We also release a test set of biomedical text for Korean-English. It consists of 758 sentences from official guidelines and recent papers, all about COVID-19.
%R 10.18653/v1/2020.nlpcovid19-2.16
%U https://aclanthology.org/2020.nlpcovid19-2.16
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.16
Markdown (Informal)
[A Multilingual Neural Machine Translation Model for Biomedical Data](https://aclanthology.org/2020.nlpcovid19-2.16) (Bérard et al., NLP-COVID19 2020)
ACL