1 research outputs found
What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions
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