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 • 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.
no code implementations • 30 May 2019 • Álvaro Peris, Francisco Casacuberta
We show that, following this framework, we approximately halve the effort spent for correcting the outputs generated by the automatic systems.
1 code implementation • ACL 2019 • Álvaro Peris, Francisco Casacuberta
We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks.
1 code implementation • CONLL 2018 • Álvaro Peris, Francisco Casacuberta
We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation.
1 code implementation • 9 Jul 2018 • Álvaro Peris, Francisco Casacuberta
We present NMT-Keras, a flexible toolkit for training deep learning models, which puts a particular emphasis on the development of advanced applications of neural machine translation systems, such as interactive-predictive translation protocols and long-term adaptation of the translation system via continuous learning.
1 code implementation • 10 Feb 2018 • Álvaro Peris, Francisco Casacuberta
We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.
1 code implementation • 10 Jun 2017 • Álvaro Peris, Luis Cebrián, Francisco Casacuberta
Neural machine translation has meant a revolution of the field.
1 code implementation • 7 Apr 2017 • Marc Bolaños, Álvaro Peris, Francisco Casacuberta, Sergi Soler, Petia Radeva
We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences.
1 code implementation • 16 Dec 2016 • Álvaro Peris, Mara Chinea-Rios, Francisco Casacuberta
We address the data selection problem in statistical machine translation (SMT) as a classification task.
1 code implementation • 12 Dec 2016 • Marc Bolaños, Álvaro Peris, Francisco Casacuberta, Petia Radeva
In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet.
1 code implementation • 12 Apr 2016 • Álvaro Peris, Marc Bolaños, Petia Radeva, Francisco Casacuberta
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions.