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Adaptive Generation in Dialogue Systems Using Dynamic User Modeling by Srinivasan Janarthanam; Oliver Lemon is a Computer Science article available to read on EtoBox.

What is Adaptive Generation in Dialogue Systems Using Dynamic User Modeling about?

We address the problem of dynamically modeling and adapting to unknown users in resource-scarce domains in the context of interactive spoken dialogue systems. As an example, we show how a system can learn to choose referring expressions to refer to domain entities for users with different levels of domain expertise, and whose domain knowledge is initially unknown to the system. We approach this problem using a three step process: collecting data using a Wizard-of-Oz method, building simulated users, and learning to model and adapt to users using Reinforcement Learning techniques. We show that by using only a small corpus of non-adaptive dialogues and user knowledge profiles it is possible to learn an adaptive user modeling policy using a sense-predict-adapt approach. Our evaluation results show that the learned user modeling and adaptation strategies performed better in terms of adaptation than some simple hand-coded baseline policies, with both simulated and real users. With real users, the learned policy produced around a 20% increase in adaptation in comparison to an adaptive hand-coded baseline. We also show that adaptation to users' domain knowledge results in improving task s

Who reads Adaptive Generation in Dialogue Systems Using Dynamic User Modeling?

Author

Srinivasan Janarthanam; Oliver Lemon

Publisher

MIT Press; Cambridge MA: MIT Press Journals; MIT Press - Journals (ISSN 0891-2017)

Published

2014

Language

EN

Field

Computer Science (Physical Sciences)