  Recent statistical approaches have improved the robustness and scalability of
spoken dialogue systems. However, despite recent progress in domain adaptation,
their reliance on in-domain data still limits their cross-domain scalability.
In this paper, we argue that this problem can be addressed by extending current
models to reflect and exploit the multi-dimensional nature of human dialogue.
We present our multi-dimensional, statistical dialogue management framework, in
which transferable conversational skills can be learnt by separating out
domain-independent dimensions of communication and using multi-agent
reinforcement learning. Our initial experiments with a simulated user show that
we can speed up the learning process by transferring learnt policies.
