One of the core challenges in Visual Dialogue problems is asking the question
that will provide the most useful information towards achieving the required
objective. Encouraging an agent to ask the right questions is difficult because
we don't know a-priori what information the agent will need to achieve its
task, and we don't have an explicit model of what it knows already. We propose
a solution to this problem based on a Bayesian model of the uncertainty in the
implicit model maintained by the visual dialogue agent, and in the function
used to select an appropriate output. By selecting the question that minimises
the predicted regret with respect to this implicit model the agent actively
reduces ambiguity. The Bayesian model of uncertainty also enables a principled
method for identifying when enough information has been acquired, and an action
should be selected. We evaluate our approach on two goal-oriented dialogue
datasets, one for visual-based collaboration task and the other for a
negotiation-based task. Our uncertainty-aware information-seeking model
outperforms its counterparts in these two challenging problems.

本篇论文提出了基于贝叶斯模型和不确定性感知的信息寻求模型，以解决视觉对话问题中 agent 提出问题的困难。实验结果表明，在两种挑战性的问题中，我们的方法都优于其它相应的方法。