• MALSORI
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• no.63
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• pp.101-112
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• 2007

Spoken dialog system development includes many laborious and inefficient tasks. Since there are many components such as speech recognition, language understanding, dialog management and knowledge management in a spoken dialog system, a developer should take an effort to edit corpus and train each model separately. To reduce a cost for editing corpus and training each model, we need more systematic and efficient working environment. For the working environment, we propose DialogStudio as a spoken dialog system workbench.

• Journal of Computing Science and Engineering
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• v.4 no.1
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• pp.1-22
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• 2010

A field of spoken dialog systems is a rapidly growing research area because the performance improvement of speech technologies motivates the possibility of building systems that a human can easily operate in order to access useful information via spoken languages. Among the components in a spoken dialog system, the dialog management plays major roles such as discourse analysis, database access, error handling, and system action prediction. This survey covers design issues and recent approaches to the dialog management techniques for modeling the dialogs. We also explain the user simulation techniques for automatic evaluation of spoken dialog systems.

• Journal of Computing Science and Engineering
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• v.2 no.1
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• pp.1-25
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• 2008

Spoken dialog tasks incur many errors including speech recognition errors, understanding errors, and even dialog management errors. These errors create a big gap between the user's intention and the system's understanding, which eventually results in a misinterpretation. To fill in the gap, people in human-to-human dialogs try to clarify the major causes of the misunderstanding to selectively correct them. This paper presents a method of clarification techniques to human-to-machine spoken dialog systems. We viewed the clarification dialog as a two-step problem-Belief confirmation and Clarification strategy establishment. To confirm the belief, we organized the clarification process into three systematic phases. In the belief confirmation phase, we consider the overall dialog system's processes including speech recognition, language understanding and semantic slot and value pairs for clarification dialog management. A clarification expert is developed for establishing clarification dialog strategy. In addition, we proposed a new design of plugging clarification dialog module in a given expert based dialog system. The experiment results demonstrate that the error verifiers effectively catch the word and utterance-level semantic errors and the clarification experts actually increase the dialog success rate and the dialog efficiency.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.311-314
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• 2007

Spoken dialog system development includes many laborious and inefficient tasks. Since there are many components such as speech recognizer, language understanding, dialog management and knowledge management in a spoken dialog system, a developer should take an effort to edit corpus and train each model separately. To reduce a cost for editting corpus and training each models, we need more systematic and efficent working environment. For the working environment, we propose DialogStudio as an spoken dialog system workbench.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.251-254
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• 2007

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.156-161
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• 2008

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.

• Journal of the Korean Society for information Management
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• v.17 no.1
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• pp.103-127
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• 2000

This study compared finding economic and energy information on the WWW to finding the same information on DIALOG, a traditional search service. Professional searchers answered 20 questions for end users using either of DIALOG and one Internet database (general search engine or Web database). The relevance of the results in both sets of answers was ranked by searchers and end-users, respectively. The study found that searching for information on the Web took at least twice as long as it did when using DIALOG. Relevance rating was a little higher for materials found on DIALOG. However, the relevance rating difference between two systems was not so higher than we expected. From the research results, we conclude that Internet database including Web database and general search engines is providing valuable information of economic and energy subject areas.

• Journal of the Korean Society for information Management
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• v.16 no.3
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• pp.7-30
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• 1999

The bibliometric analysis was conducted by searching 87 abstract & index databases provided by the DIALOG to identify not only the characteristics of Korean journals covered by international databases, but also the characteristics of the databases covering such Korean journals. 248 Korean journals were identified, for which at least one paper published between 1990 and 1997 was retrievable from the DIALOG databases. These Korean journals were found in 52 databases. 141 journals(56.9%) were indexed in only one database. Most Korean journals covered in several DIALOG databases were science and engineering journals published in English. The difference in the degrees Korean journals are covered in a database is found to be statistically significant by the discipline a database covers, and by the database's journal selection policy.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.1-8
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• 2019

Dialog system is becoming a new dominant interaction way between human and computer. It allows people to be provided with various services through natural language. The dialog system has a common structure of a pipeline consisting of several modules (e.g., speech recognition, natural language understanding, and dialog management). In this paper, we tackle a task of domain classification for the natural language understanding module by employing machine learning models such as convolutional neural network and random forest. For our dataset of seven service domains, we showed that the random forest model achieved the best performance (F1 score 0.97). As a future work, we will keep finding a better approach for domain classification by investigating other machine learning models.

• Journal of Information Management
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• v.15 no.1
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• pp.29-41
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• 1982