Learning to Interrupt the User at the Right Time in Incremental Dialogue Systems

Continuous processing of input in incremental dialogue systems might result in the need of interrupting a user’s utterance when clarification or rapport is needed. Being able to predict the right time when to interrupt the utterance can be another step to a more human-like dialogue. On the other hand, annotation of corpora with different types of possible interruptions requires additional human resources. In this paper, we discuss how to process a corpus that does not have interruptions specifically annotated. We also present initial experiments on two corpora and show that it is possible to model the desired behaviour from these corpora.