  Dialogue management (DM) plays a key role in the quality of the interaction
with the user in a task-oriented dialogue system. In most existing approaches,
the agent predicts only one DM policy action per turn. This significantly
limits the expressive power of the conversational agent and introduces unwanted
turns of interactions that may challenge users' patience. Longer conversations
also lead to more errors and the system needs to be more robust to handle them.
In this paper, we compare the performance of several models on the task of
predicting multiple acts for each turn. A novel policy model is proposed based
on a recurrent cell called gated Continue-Act-Slots (gCAS) that overcomes the
limitations of the existing models. Experimental results show that gCAS
outperforms other approaches. The code is available at
https://leishu02.github.io/
