Towards Learning to Converse: Structuring Task-Oriented Human-Human Dialogs

Data-driven techniques have influenced many aspects of speech and language processing. Models derived from data are generally more robust than hand-crafted systems since they better reflect the distributions of the phenomena being modeled. With the availability of large spoken dialog corpora, dialog management can now reap the benefit of data-driven techniques. In this paper, we present our view of structuring human-human dialogs in order to learn models for human-machine dialogs. We present the problems of dialog segmentation and dialog act labeling, develop a model for predicting and labeling topic segments and dialog acts and evaluate the model on customer-agent dialogs from a catalog service domain

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