Paper: Learning Effective Multimodal Dialogue Strategies from Wizard-of-Oz Data: Bootstrapping and Evaluation

ACL ID P08-1073
Title Learning Effective Multimodal Dialogue Strategies from Wizard-of-Oz Data: Bootstrapping and Evaluation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

We address two problems in the field of au- tomatic optimization of dialogue strategies: learning effective dialogue strategies when no initial data or system exists, and evaluating the result with real users. We use Reinforcement Learning (RL) to learn multimodal dialogue strategies by interaction with a simulated envi- ronment which is “bootstrapped” from small amounts of Wizard-of-Oz (WOZ) data. This use of WOZ data allows development of op- timal strategies for domains where no work- ing prototype is available. We compare the RL-based strategy against a supervised strat- egy which mimics the wizards’ policies. This comparison allows us to measure relative im- provement over the training data. Our results show that RL significantly outperforms Super- vised Learning when interactin...