What Should I Ask? Using Conversationally Informative Rewards for
Goal-Oriented Visual Dialogue
Abstract
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 goaldriven 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