Paper: Learning to Adapt to Unknown Users: Referring Expression Generation in Spoken Dialogue Systems

ACL ID P10-1008
Title Learning to Adapt to Unknown Users: Referring Expression Generation in Spoken Dialogue Systems
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

We present a data-driven approach to learn user-adaptive referring expression gener- ation (REG) policies for spoken dialogue systems. Referring expressions can be dif- ficult to understand in technical domains where users may not know the techni- cal ‘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 sys- tem learns REG policies which can adapt to unknown users online. Furthermore, unlike supervised learning methods which require a large corpus of expert adaptive behaviour to train on, we show that effec- tive adaptive policies can be learned from a small dialogue corpus of non-adaptive human-machin...