Current tum-taking approaches for spoken dialogue systems rely on the speaker releasing the turn before the other can take it.
As spoken dialogue systems are designed to perform ever more elaborate tasks, the need for mixed-initiative interaction necessarily grows.
Current dialogue systems focus on the release-turn as the most important aspect of turn-taking, in which a listener will only take the turn after the speaker has released it.
We introduce the IDTB model to overcome the deficiencies of current approaches.
We build our dialogue system using the Information State Update approach (Larsson and Traum, 2000) and use Reinforcement Learning for action selection (Sutton and Barto, 1998).
In this section, we show how the IDTB approach can be implemented for a collaborative slot filling domain.