no code implementations • EAMT 2022 • Eirini Kaldeli, Mercedes García-Martínez, Antoine Isaac, Paolo Sebastiano Scalia, Arne Stabenau, Iván Lena Almor, Carmen Grau Lacal, Martín Barroso Ordóñez, Amando Estela, Manuel Herranz
Europeana Translate is a project funded under the Connecting European Facility with the objective to take advantage of state-of-the-art machine translation in order to increase the multilinguality of resources in the cultural heritage domain
no code implementations • EAMT 2020 • Miguel Domingo, Mercedes García-Martínez, Álvaro Peris, Alexandre Helle, Amando Estela, Laurent Bié, Francisco Casacuberta, Manuel Herranz
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 • EAMT 2020 • 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 • EAMT 2020 • 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.
no code implementations • WS 2019 • Miguel Domingo, Mercedes García-Martínez, Álvaro Peris, Alexandre Helle, Amando Estela, Laurent Bié, Francisco Casacuberta, Manuel Herranz
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.
1 code implementation • ACL 2019 • Miguel Domingo, Mercedes García-Martínez, Amando Estela, Laurent Bié, Alexandre Helle, Álvaro Peris, Francisco Casacuberta, Manuerl Herranz
We introduce a demonstration of our system, which implements online learning for neural machine translation in a production environment.