Simon Keizer

Transfer Learning for Conversational AI: a Multidimensional Approach

The emergence of deep learning and recent advances in using large pre-trained language models have given conversational AI a big impulse. For a range of applications, models can be fine-tuned with only a limited amount of application specific data. However, for scalable development of spoken dialogue interfaces with true conversational intelligence, ultimately this approach may very well prove to be insufficient. In this presentation, I will argue for a multi-dimensional dialogue modelling approach, and discuss experiments, in which its transfer learning potential is demonstrated for the case of adapting reinforcement learning-based dialogue policies to a new application.

Simon Keizer is a research scientist specialized in conversational AI. He has a BSc/MSc in Applied Mathematics and a PhD in Computer Science, both from the University of Twente (The Netherlands). Simon has been active in spoken dialogue systems research for more than 18 years, focusing on topics such as statistical dialogue management, reinforcement learning, and user simulation. He has been involved in several national and international research projects at Tilburg University, University of Cambridge, Heriot-Watt University Edinburgh and the Vrije Universiteit Brussel, and is currently a research engineer in the Cambridge Research Lab, Toshiba Europe Limited.

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