Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling

TL;DR

This paper proposes a multi-dimensional dialogue modeling framework that enhances transferability of conversational skills across domains using multi-agent reinforcement learning, demonstrated through initial simulated experiments.

Contribution

It introduces a novel multi-dimensional, statistical dialogue management framework that separates domain-independent communication aspects and employs reinforcement learning for transferability.

Findings

Transfer of learned policies speeds up learning process

Multi-dimensional modeling captures human dialogue complexity

Initial experiments show promising transfer capabilities

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.