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Adaptive Generation in Dialogue Systems Using Dynamic User Modeling
2014
Computational Linguistics
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
doi:10.1162/coli_a_00203
fatcat:3v3wetxiabd3zhtmta3zsdflvq