A statistical prediction model of speakers' intentions using multi-level features in a goal-oriented dialog system

Authors:
Choong-Nyoung Seon;Harksoo Kim;Jungyun Seo
Affiliations:
Department of Computer Science, Sogang University, Republic of Korea;Program of Computer and Communications Engineering, Kangwon National University, Republic of Korea;Department of Computer Science & Interdisciplinary Program of Integrated Biotechnology, Sogang University, Republic of Korea
Venue:
Pattern Recognition Letters
Year:
2012

Citing 11
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Abstract

A dialog system is an intelligent program that helps users easily access information stored in a knowledge base by formulating requests in their natural language. A dialog system needs an intention prediction module for use as a preprocessor to reduce the search space of an automatic speech recognizer. To satisfy these needs, we propose a statistical model to predict speakers' intentions. The proposed model represents a dialog history, with various levels of linguistic features. The proposed model predicts the user's next intention by giving the linguistic features as inputs to a statistical machine learning model. In experiments conducted in a schedule management domain, the proposed model showed a higher average precision than the previous model.