Paper: Learning More Effective Dialogue Strategies Using Limited Dialogue Move Features

ACL ID P06-1024
Title Learning More Effective Dialogue Strategies Using Limited Dialogue Move Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

We explore the use of restricted dialogue contexts in reinforcement learning (RL) of effective dialogue strategies for infor- mation seeking spoken dialogue systems (e.g. COMMUNICATOR (Walker et al. , 2001)). The contexts we use are richer than previous research in this area, e.g. (Levin and Pieraccini, 1997; Schef er and Young, 2001; Singh et al. , 2002; Pietquin, 2004), which use only slot-based infor- mation, but are much less complex than the full dialogue Information States ex- plored in (Henderson et al. , 2005), for which tractabe learning is an issue. We explore how incrementally adding richer features allows learning of more effective dialogue strategies. We use 2 user simu- lations learned from COMMUNICATOR data (Walker et al. , 2001; Georgila et al. , 2005b) to explore the effec...