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By Esther Levin and Roberto Pieraccini


In this paper we address the problem of optimizing the decisions taken by a spoken dialog system based on a given business model. Although the model can be applied to other types of decisions, we refer to the choice on whether to continue a session or to escalate it to a human agent. We consider a family of business models based on costs and savings that are dependent on the decision taken and the automation outcome of each session. Based on a corpus of more than 61,000 dialogs, we show that an optimal classifier can be selected to minimize the cost for a given business model. 1

Year: 2009
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