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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. | Robust Dialog Management with N-best Hypotheses Using Dialog Examples and Agenda Cheongjae Lee Sangkeun Jung and Gary Geunbae Lee Pohang University of Science and Technology Department of Computer Science and Engineering Pohang Republic of Korea lcj80 hugman gblee @postech.ac.kr Abstract 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. 1 Introduction Development of spoken dialog systems involves human language technologies which must cooperate to answer user queries. Since the performance in human language technologies such as Automatic Speech Recognition ASR and Natural Language Understanding NLU 1 have been improved this advance has made it possible to develop spoken dialog systems for many different application domains. Nevertheless there are major problems for practical spoken dialog systems. One of them which must be considered by the Dialog Manager DM is the error propagation from ASR and NLU modules. In Through this paper we will use the term natural language to include both spoken language and written language general errors in spoken dialog systems are prevalent due to errors in speech recognition or language understanding. These errors can cause the dialog system to misunderstand a user and in turn lead to an inappropriate response. To avoid these errors a basic solution is to improve the accuracy and robustness of the .
