Robust Dialog Management with N-Best Hypotheses Using Dialog Examples and Agenda

This work presents an agenda-based approach to improve the robustness of the dialog manager by using dialog examples and n-best recognition hypotheses. This approach supports n-best hypotheses in the dialog manager and keeps track of the dialog state using a discourse interpretation algorithm with the agenda graph and focus stack. Given the agenda graph and n-best hypotheses, the system can predict the next system actions to maximize multi-level score functions. To evaluate the proposed method, a spoken dialog system for a building guidance robot was developed. Preliminary evaluation shows this approach would be effective to improve the robustness of example-based dialog modeling.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Cheongjae Lee, Sangkeun Jung, Gary Geunbae Lee
• Keywords: ACL 2008, Agenda Graph, Computational Linguistics, Dialog, Dialog Manager