Quanto siamo vicini a rimuovere le barriere linguistiche nel settore sanitario
Marco Trombetti
DOI: DOI: 10.36158/97888929564072
The article has been translated automatically. Note
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1. https://blogs.ec.europa.eu/emt/cOVID-19-HOW-HAS-IT- affected-the-world-of-translation
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2. DuPont Q. (2018), The Cryptological Origins of Machine Translation: From al-Kindi to Weaver, «Amodern», Issue 8, Translation-Machination.
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3. https://www.semanticscholar.org/paper/The-first- public-demonstration-of-machine-%3A-the-%2C-7- HUTCHINS/AD014A7B7A3142E6F17EeDDDF4218489B56AB18E.
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4. https://modernmt.com. More information on the project at https://slator.com/business-academia-join-forc- es-launch-next-gen-machine-translation/.
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5. EU Horizon 2020 Innovation Action (2015-2017).
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6. MateCat, acronym of Machine Translation En- hanced Computer Assisted Translation, is a 3-year research project (11/2011 – 10/2014) funded by the Europe- an Union’s Seventh Framework Programme for research, technological development and demonstration
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https://matecat.com.
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https://translated.com/t-rank.
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https://openai.com/blog/ai-and-efficiency/.
