Automatically Training a Problematic Dialogue Predictor for a Spoken
  Dialogue System
  Spoken dialogue systems promise efficient and natural access to a large
variety of information sources and services from any phone. However, current
spoken dialogue systems are deficient 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' (SM) 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 first two
exchanges in the dialogue can predict problematic dialogues 13.2% more
accurately than the baseline.
