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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/9661
Title: Assessing User Expertise in Spoken Dialog System Interactions
Authors: Ribeiro, Eugenio
Batista, Fernando
Trancoso, Isabel
Lopes, Jose
Ribeiro, Ricardo
de Matos, David
Keywords: User expertise
Let’s Go
SVM
Random Forest
Issue Date: 2016
Publisher: Springer
Citation: Ribeiro E., Batista F., Trancoso I., Lopes J., Ribeiro R., de Matos D.M. (2016) Assessing User Expertise in Spoken Dialog System Interactions. In: Abad A. et al. (eds) Advances in Speech and Language Technologies for Iberian Languages. IberSPEECH 2016. Lecture Notes in Computer Science, vol 10077. Springer
Series/Report no.: Lecture Notes in Computer Science;vol 10077
Abstract: Identifying the level of expertise of its users is important for a system since it can lead to a better interaction through adaptation techniques. Furthermore, this information can be used in offline processes of root cause analysis. However, not much effort has been put into automatically identifying the level of expertise of an user, especially in dialog-based interactions. In this paper we present an approach based on a specific set of task related features. Based on the distribution of the features among the two classes – Novice and Expert – we used Random Forests as a classification approach. Furthermore, we used a Support Vector Machine classifier, in order to perform a result comparison. By applying these approaches on data from a real system, Let’s Go, we obtained preliminary results that we consider positive, given the difficulty of the task and the lack of competing approaches for comparison.
URI: http://hdl.handle.net/1820/9661
Appears in Collections:1. RAGE Publications

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