Attention, intentions, and the structure of discourse
Computational Linguistics
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
The reliability of a dialogue structure coding scheme
Computational Linguistics
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Data-driven strategies for an automated dialogue system
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Learning the structure of task-driven human-human dialogs
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
You are what you say: using meeting participants' speech to detect their roles and expertise
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
Topic segmentation of dialogue
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
Triggering effective social support for online groups
ACM Transactions on Interactive Intelligent Systems (TiiS)
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We 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 task-oriented domains, including information-access 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. With some modifications to make these algorithms more suitable for inferring the structure of a spoken dialog, the unsupervised learning algorithms show promise.