We study the application of active learning techniques to the translation of
unbounded data streams via interactive neural machine translation. The main
idea is to select, from an unbounded stream of source sentences, those worth to
be supervised by a human agent...
The user will interactively translate those
samples. Once validated, these data is useful for adapting the neural machine
translation model. We propose two novel methods for selecting the samples to be validated. We
exploit the information from the attention mechanism of a neural machine
translation system. Our experiments show that the inclusion of active learning
techniques into this pipeline allows to reduce the effort required during the
process, while increasing the quality of the translation system. Moreover, it
enables to balance the human effort required for achieving a certain
translation quality. Moreover, our neural system outperforms classical
approaches by a large margin.