  Despite the advances achieved by neural models in sequence to sequence
learning, exploited in a variety of tasks, they still make errors. In many use
cases, these are corrected by a human expert in a posterior revision process.
The interactive-predictive framework aims to minimize the human effort spent on
this process by considering partial corrections for iteratively refining the
hypothesis. In this work, we generalize the interactive-predictive approach,
typically applied in to machine translation field, to tackle other multimodal
problems namely, image and video captioning. We study the application of this
framework to multimodal neural sequence to sequence models. We show that,
following this framework, we approximately halve the effort spent for
correcting the outputs generated by the automatic systems. Moreover, we deploy
our systems in a publicly accessible demonstration, that allows to better
understand the behavior of the interactive-predictive framework.
