Way, Andy ORCID: 0000-0001-5736-5930, Haque, Rejwanul ORCID: 0000-0003-1680-0099, Xie, Guodong ORCID: 0000-0002-5328-1585, Gaspari, Federico ORCID: 0000-0003-3808-8418, Popović, Maja ORCID: 0000-0001-8234-8745 and Poncelas, Alberto ORCID: 0000-0002-5089-1687 (2020) Rapid development of competitive translation engines for access to multilingual COVID-19 information. Informatics . ISSN 2227-9709
Abstract
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.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
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 Initiatives and Centres > ADAPT |
Publisher: | MDPI |
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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