A Multilingual Neural Machine Translation Model for Biomedical Data
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.
Code (1)
Tasks
Machine TranslationTranslationSimilar Papers 제목 키워드 기반
FJWU Participation for the WMT21 Biomedical Translation Task
In this paper we present the FJWU’s system submitted to the biomedical shared task at WMT21. We prepared state-of-the-art multilingual neural machine translation systems for three languages (i.e. German, Spanish and Fren…
Domain AdaptationInformation RetrievalMachine TranslationNMT+2Multilingual enrichment of disease biomedical ontologies
Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on…
Machine TranslationTranslationCollaboratively Annotating Multilingual Parallel Corpora in the Biomedical Domain---some MANTRAs
The coverage of multilingual biomedical resources is high for the English language, yet sparse for non-English languages―an observation which holds for seemingly well-resourced, yet still dramatically low-resourced one…
Named Entity Recognition (NER)TranslationNaver Labs Europe’s Participation in the Robustness, Chat, and Biomedical Tasks at WMT 2020
This paper describes Naver Labs Europe’s participation in the Robustness, Chat, and Biomedical Translation tasks at WMT 2020. We propose a bidirectional German-English model that is multi-domain, robust to noise, and whi…
Language ModelingLanguage ModellingTranslationLMU Munich's Neural Machine Translation Systems at WMT 2018
We present the LMU Munich machine translation systems for the English{--}German language pair. We have built neural machine translation systems for both translation directions (English→German and German→English) and for …
Domain AdaptationMachine TranslationTranslationUnsupervised Machine Translation