Experiments with Computational Models of Turn-Taking

Abstract

Deciding when to take (or not to take) the turn in a conversation is an important task. It has been stressed in the descriptive literature that such decisions must involve prediction, as they often seem to be made before a transition place has been reached. In computational systems, however, turn-taking is normally a reaction to parameters like pause length. In this paper, we report on experiments that try to bridge this gap. We describe an experiment (using controlled stimuli) that shows human performance at prediction of turn-taking decisions and then show that a model automatically induced from data can reach a similar level of performance. We then describe a series of experiments on spontaneous dialogue data where we combine pause thresholds with syntactic and prosodic information to make turn-taking decisions, successively reducing the pause threshold until reaction becomes prediction. All our classifiers improve significantly over the baselines; prediction however is shown to be the hardest task, and we discuss additional information sources that could improve it.