Adaptive neural machine translation systems, able to incrementally update the underlying models under an online learning regime, have been proven to be useful to improve the efficiency of this workflow.
no code implementations • • Ēriks Ajausks, Victoria Arranz, Laurent Bié, Aleix Cerdà-i-Cucó, Khalid Choukri, Montse Cuadros, Hans Degroote, Amando Estela, Thierry Etchegoyhen, Mercedes García-Martínez, Aitor García-Pablos, Manuel Herranz, Alejandro Kohan, Maite Melero, Mike Rosner, Roberts Rozis, Patrick Paroubek, Artūrs Vasiļevskis, Pierre Zweigenbaum
We describe the MAPA project, funded under the Connecting Europe Facility programme, whose goal is the development of an open-source de-identification toolkit for all official European Union languages.
no code implementations • • Laurent Bié, Aleix Cerdà-i-Cucó, Hans Degroote, Amando Estela, Mercedes García-Martínez, Manuel Herranz, Alejandro Kohan, Maite Melero, Tony O’Dowd, Sinéad O’Gorman, Mārcis Pinnis, Roberts Rozis, Riccardo Superbo, Artūrs Vasiļevskis
The Neural Translation for the European Union (NTEU) project aims to build a neural engine farm with all European official language combinations for eTranslation, without the necessity to use a high-resourced language as a pivot.
We introduce a demonstration of our system, which implements online learning for neural machine translation in a production environment.
A common use of machine translation in the industry is providing initial translation hypotheses, which are later supervised and post-edited by a human expert.