Acquiring Domain-Specific Dialog Information from Task-Oriented Human-Human Interaction through an Unsupervised LearningProceedings of the 2008 Conference on Empirical Methods in Natural Language Processing
Date of Original Version1-1-2008
Abstract or DescriptionWe describe an approach for acquiring the domain-specific dialog knowledge required to configure a task-oriented dialog system that uses human-human interaction data. The key aspects of this problem are the design of a dialog information representation and a learning approach that supports capture of domain information from in-domain dialogs. To represent a dialog for a learning purpose, we based our representation, the form-based dialog structure representation, on an observable structure. We show that this representation is sufficient for modeling phenomena that occur regularly in several dissimilar taskoriented domains, including informationaccess and problem-solving. With the goal of ultimately reducing human annotation effort, we examine the use of unsupervised learning techniques in acquiring the components of the form-based representation (i.e. task, subtask, and concept). These techniques include statistical word clustering based on mutual information and Kullback-Liebler distance, TextTiling, HMM-based segmentation, and bisecting K-mean document clustering. Withsome modifications to make these algorithms more suitable for inferring the structure of a spoken dialog, the unsupervised learning algorithms show promise.
Citation InformationAlexander I Rudnicky and Ananlada Chotimongkol. "Acquiring Domain-Specific Dialog Information from Task-Oriented Human-Human Interaction through an Unsupervised Learning" Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (2008) p. 955 - 964
Available at: http://works.bepress.com/alexander_rudnicky/79/