Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling [article]

Simon Keizer, Verena Rieser
<span title="2018-03-31">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... ational 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1804.00146v1</a> <a target="_blank" rel="external noopener" href="">fatcat:xdojv2w52jc47g6zitenneejze</a> </span>
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