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Learning Adaptive Referring Expression Generation Policies for Spoken Dialogue Systems
[chapter]
2010
Lecture Notes in Computer Science
We present new results from a real-user evaluation of a data-driven approach to learning user-adaptive referring expression generation (REG) policies for spoken dialogue systems. Referring expressions can be difficult to understand in technical domains where users may not know the technical 'jargon' names of the domain entities. In such cases, dialogue systems must be able to model the user's (lexical) domain knowledge and use appropriate referring expressions. We present a reinforcement
doi:10.1007/978-3-642-15573-4_4
fatcat:46re3tqombbapcj3eybsusky44