Adaptive Referring Expression Generation in Spoken Dialogue Systems: Evaluation with Real Users

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 learning (RL) framework in which the system learns REG policies which can adapt to unknown users online. For real users of such a system, we show that in comparison to an adaptive hand-coded baseline policy, the learned policy performs significantly better, with a 20.8% average increase in adaptation accuracy, 12.6% decrease in time taken, and a 15.1% increase in task completion rate. The learned policy also has a significantly better subjective rating from users. This is because the learned...

• Type: Journal
• Year: 2010
• Where: SIGDIAL
• Authors: Srinivasan Janarthanam, Oliver Lemon
• Keywords: Appropriate Referring Expressions, Dialogue Systems, Natural Language Processing, Real Users, SIGDIAL 2010