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.
We show that, following this framework, we approximately halve the effort spent for correcting the outputs generated by the automatic systems.
We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks.
We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation.
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.
We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.
We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences.
We address the data selection problem in statistical machine translation (SMT) as a classification task.
In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet.
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions.