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Rapid development of competitive translation engines for access to multilingual COVID-19 information

Way, Andy orcid logoORCID: 0000-0001-5736-5930, Haque, Rejwanul orcid logoORCID: 0000-0003-1680-0099, Xie, Guodong orcid logoORCID: 0000-0002-5328-1585, Gaspari, Federico orcid logoORCID: 0000-0003-3808-8418, Popović, Maja orcid logoORCID: 0000-0001-8234-8745 and Poncelas, Alberto orcid logoORCID: 0000-0002-5089-1687 (2020) Rapid development of competitive translation engines for access to multilingual COVID-19 information. Informatics . ISSN 2227-9709

Every day, more people are becoming infected and dying from exposure to COVID-19. Some countries in Europe like Spain, France, the UK and Italy have suffered particularly badly from the virus. Others such as Germany appear to have coped extremely well. Both health professionals and the general public are keen to receive up-to-date information on the effects of the virus, as well as treatments that have proven to be effective. In cases where language is a barrier to access of pertinent information, machine translation (MT) may help people assimilate information published in different languages. Our MT systems trained on COVID-19 data are freely available for anyone to use to help translate information (such as promoting good practice for symptom identification, prevention, and treatment) published in German, French, Italian, Spanish into English, as well as the reverse direction.
Item Type:Article (Published)
Uncontrolled Keywords:COVID-19; crisis translation; neural MT; automatic evaluation; human evaluation; online MT; rapid response MT
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
Computer Science > Information technology
Computer Science > Machine learning
Computer Science > Machine translating
Humanities > Translating and interpreting
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Official URL:http://dx.doi.org/10.3390/informatics7020019
Copyright Information:© 2020 The Authors. Open Access (CC BY 4.0)
Funders:Science Foundation Ireland (SFI) (Grant No. 13/RC/2106), European Regional Development Fund
ID Code:24590
Deposited On:26 Jun 2020 10:50 by Andrew Way . Last Modified 05 May 2023 16:42

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