Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling

2018/03/31 by Keizer, Simon, Rieser, Verena
#Computation and Language (cs.CL) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.1804.00146

Abstract

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.

Related

• Transferable Dialogue Systems and User Simulators
• Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
• User Evaluation of a Multi-dimensional Statistical Dialogue System
• Dialogue manager domain adaptation using Gaussian process reinforcement learning
• Domain Transfer in Dialogue Systems without Turn-Level Supervision