  Syntactic and pragmatic completeness is known to be important for turn-taking
prediction, but so far machine learning models of turn-taking have used such
linguistic information in a limited way. In this paper, we introduce TurnGPT, a
transformer-based language model for predicting turn-shifts in spoken dialog.
The model has been trained and evaluated on a variety of written and spoken
dialog datasets. We show that the model outperforms two baselines used in prior
work. We also report on an ablation study, as well as attention and gradient
analyses, which show that the model is able to utilize the dialog context and
pragmatic completeness for turn-taking prediction. Finally, we explore the
model's potential in not only detecting, but also projecting, turn-completions.
