  This paper investigates the application of machine learning (ML) techniques
to enable intelligent systems to learn multi-party turn-taking models from
dialogue logs. The specific ML task consists of determining who speaks next,
after each utterance of a dialogue, given who has spoken and what was said in
the previous utterances. With this goal, this paper presents comparisons of the
accuracy of different ML techniques such as Maximum Likelihood Estimation
(MLE), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN)
architectures, with and without utterance data. We present three corpora: the
first with dialogues from an American TV situated comedy (chit-chat), the
second with logs from a financial advice multi-bot system and the third with a
corpus created from the Multi-Domain Wizard-of-Oz dataset (both are
topic-oriented). The results show: (i) the size of the corpus has a very
positive impact on the accuracy for the content-based deep learning approaches
and those models perform best in the larger datasets; and (ii) if the dialogue
dataset is small and topic-oriented (but with few topics), it is sufficient to
use an agent-only MLE or SVM models, although slightly higher accuracies can be
achieved with the use of the content of the utterances with a CNN model.
