Dynamic Dialogue Policy for Continual Reinforcement Learning

By Christian Geishauser, Carel van Niekerk, Nurul Lubis, Michael Heck, Hsien-Chin Lin, Shutong Feng, Milica Gavsi'c. 2022

19 citations recorded by the source corpus.

Continual learning is one of the key components of human learning and a necessary requirement of artificial intelligence. As dialogue can potentially span infinitely many topics and tasks, a task-oriented dialogue system must have the capability to continually learn, dynamically adapting to new challenges while preserving the knowledge it already acquired. Despite the importance, continual reinforcement learning of the dialogue policy has remained largely unaddressed. The lack of a framework with training protocols, baseline models and suitable metrics, has so far hindered research in this direction. In this work we fill precisely this gap, enabling research in dialogue policy optimisation to go from static to dynamic learning. We provide a continual learning algorithm, baseline architectures and metrics for assessing continual learning models. Moreover, we propose the dynamic dialogue policy transformer (DDPT), a novel dynamic architecture that can integrate new knowledge seamlessly, is capable of handling large state spaces and obtains significant zero-shot performance when being exposed to unseen domains, without any growth in network parameter size. We validate the strengths of DDPT in simulation with two user simulators as well as with humans.

Subfields: Artificial Intelligence, Reinforcement Learning, Dialogue & Conversational AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Hang Su
  2. Jun Zhu
  3. Liyuan Wang
  4. Xingxing Zhang
  5. Tianyu Pang
  6. Yinpeng Dong
  7. Vamsi Krishna Ithapu
  8. Mirella Lapata

In Reinforcement Learning

  1. Liyuan Wang
  2. Jun Zhu
  3. Hang Su
  4. Xingxing Zhang

Citations in the corpus