Is it possible to predict task completion in automated troubleshooters?

Thede online prediction of task success in Interactive Voice Response (IVR) systems is a comparatively new field of research. It helps to identify problemantic calls and enables the dialog system to react before the caller gets overly frustrated. This publication investigates, to which extent it is possible to predict task completion and how existing approaches generalize for long dialogs. We compare the performance of two different modeling techniques: linear modeling and n-gram modeling. We show that n-gram modeling outperforms linear modeling significantly at later prediction points. From a comprehensive set of interaction parameters, we identify the relevant ones using the Information Gain Ratio. New interaction parameters are presented and evaluated. The study is based on 41,422 calls from an automated Internet troubleshooter with an average of 21.4 turns per call.