Assessing User Expertise in Spoken Dialog System Interactions
This addresses the need for adaptive systems and root cause analysis in dialog-based interactions, but it is incremental as it builds on existing classification methods without major breakthroughs.
The paper tackled the problem of automatically identifying user expertise in spoken dialog system interactions, using task-related features and Random Forests with SVM comparison, and reported preliminary positive results on data from the Let's Go system.
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.