# Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling

arXiv: https://arxiv.org/abs/1804.00146 · HTML: https://mlsanity.com/paper/1804.00146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling](https://mlsanity.com/paper/2204.07082/) (2022)

## Abstract (arXiv)

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.