Assessing user expertise in spoken dialog system interactions

• 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Annan teknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Other Engineering and Technologies (hsv//eng)

Nyckelord

Let’s Go

Random forest

SVM

User expertise

Decision trees

Adaptation techniques

Classification approach

Random forests

Result comparison

Root cause analysis

Spoken dialog systems

Support vector machine classifiers

Support vector machines

Publikations- och innehållstyp

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