Automatically Training a Problematic Dialogue Predictor for a Spoken Dialogue System

Spoken dialogue systems promise eecient and natural access to a large variety of information sources and services from any phone. However, current spoken dialogue systems are deecient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic human-computer dialogues using a corpus of 4692 dialogues collected with the How May I Help You ^ fsmg spoken dialogue system. The Problematic Dialogue Predictor can be immediately applied to the system's decision of whether to transfer the call to a human customer care agent, or be used as a cue to the system's dialogue manager to modify its behavior to repair problems, and even perhaps, to prevent them. We show that a Problematic Dialogue Predictor using automatically-obtainable features from the rst two exchanges in the dialogue can predict problematic dialogues 13.2% more accurately than the baseline.