Conference Paper

A Situation-Based Dialogue Management using Dialogue Examples

Dept. of Comput. Sci. & Eng., Pohang Sci. & Technol. Univ.
DOI: 10.1109/ICASSP.2006.1659959 Conference: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, Volume: 1
Source: IEEE Xplore

ABSTRACT In this paper, we present POSTECH Situation-Based Dialogue Manager (POSSDM) for a spoken dialogue system using both example- and rule-based dialogue management techniques for effective generation of appropriate system responses. A spoken dialogue system should generate cooperative responses to smoothly control dialogue flow with the users. We introduce a new dialogue management technique incorporating dialogue examples and situation-based rules for the electronic program guide (EPG) domain. For the system response generation, we automatically construct and index a dialogue example database from the dialogue corpus, and the proper system response is determined by retrieving the best dialogue example for the current dialogue situation, which includes a current user utterance, dialogue act, semantic frame and discourse history. When the dialogue corpus is not enough to cover the domain, we also apply manually constructed situation-based rules mainly for meta-level dialogue management. Experiments show that our example-based dialogue modeling is very useful and effective in domain-oriented dialogue processing

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    • "The basic idea of our approach is that a dialog manager (DM) uses dialog examples that are semantically indexed to a database, instead of domain-specific rules or probabilistic models for dialog management. We have presented the EBDM methodology for a goal-oriented dialog system in a single electronic program guide (EPG) domain (Lee et al., 2006). "
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    ABSTRACT: This paper proposes a generic dialog modeling framework for a multi-domain dialog system to simultaneously manage goal-oriented and chat dialogs for both information access and entertainment. We developed a dialog modeling technique using an example-based approach to implement multiple applications such as car navigation, weather information, TV program guidance, and chatbot. Example-based dialog modeling (EBDM) is a simple and effective method for prototyping and deploying of various dialog systems. This paper also introduces the system architecture of multi-domain dialog systems using the EBDM framework and the domain spotting technique. In our experiments, we evaluate our system using both simulated and real users. We expect that our approach can support flexible management of multi-domain dialogs on the same framework.
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    • "Our error recovery strategy is implemented based on an Example- Based Dialog Modeling (EBDM) which is one of generic dialog modelings technology [8]. We begin with a brief overview of the EBDM framework in this section. "
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    ABSTRACT: Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error recovery is focused on the re-phrase strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system.
    Automatic Speech Recognition & Understanding, 2007. ASRU. IEEE Workshop on; 01/2008
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    • "In recent, we proposed another data-driven approach for the dialog modeling called Examplebased Dialog Modeling (EBDM) (Lee et al., 2006a). However, difficulties occur when attempting to deploy EBDM in practical spoken dialog systems in which ASR and NLU errors are frequent. "
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    ABSTRACT: This work presents an agenda-based approach to improve the robustness of the dialog man- ager by using dialog examples and n-best recognition hypotheses. This approach sup- ports n-best hypotheses in the dialog man- ager and keeps track of the dialog state us- ing 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 dia- log system for a building guidance robot was developed. Preliminary evaluation shows this approach would be effective to improve the ro- bustness of example-based dialog modeling.
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