  The ability to engage in goal-oriented conversations has allowed humans to
gain knowledge, reduce uncertainty, and perform tasks more efficiently.
Artificial agents, however, are still far behind humans in having goal-driven
conversations. In this work, we focus on the task of goal-oriented visual
dialogue, aiming to automatically generate a series of questions about an image
with a single objective. This task is challenging since these questions must
not only be consistent with a strategy to achieve a goal, but also consider the
contextual information in the image. We propose an end-to-end goal-oriented
visual dialogue system, that combines reinforcement learning with regularized
information gain. Unlike previous approaches that have been proposed for the
task, our work is motivated by the Rational Speech Act framework, which models
the process of human inquiry to reach a goal. We test the two versions of our
model on the GuessWhat?! dataset, obtaining significant results that outperform
the current state-of-the-art models in the task of generating questions to find
an undisclosed object in an image.
