no code implementations • • 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
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.
no code implementations • 14 Nov 2022 • Francisco Casacuberta, Alexandru Ceausu, Khalid Choukri, Miltos Deligiannis, Miguel Domingo, Mercedes García-Martínez, Manuel Herranz, Guillaume Jacquet, Vassilis Papavassiliou, Stelios Piperidis, Prokopis Prokopidis, Dimitris Roussis, Marwa Hadj Salah
This work presents the results of the machine translation (MT) task from the Covid-19 MLIA @ Eval initiative, a community effort to improve the generation of MT systems focused on the current Covid-19 crisis.
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.
FNMT system is designed to manage larger vocabulary and reduce the training time (for systems with equivalent target language vocabulary size).
This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation.
This paper describes LIUM submissions to WMT17 News Translation Task for English-German, English-Turkish, English-Czech and English-Latvian language pairs.
nmtpy has been used for LIUM's top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.
This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge.
no code implementations • • Vicent Alabau, Christian Buck, Michael Carl, Francisco Casacuberta, Mercedes García-Martínez, Ulrich Germann, Jesús González-Rubio, Robin Hill, Philipp Koehn, Luis Leiva, Bartolomé Mesa-Lao, Daniel Ortiz-Martínez, Herve Saint-Amand, Germán Sanchis Trilles, Chara Tsoukala