  In a human-machine dialog scenario, deciding the appropriate time for the
machine to take the turn is an open research problem. In contrast, humans
engaged in conversations are able to timely decide when to interrupt the
speaker for competitive or non-competitive reasons. In state-of-the-art
turn-by-turn dialog systems the decision on the next dialog action is taken at
the end of the utterance. In this paper, we propose a token-by-token prediction
of the dialog state from incremental transcriptions of the user utterance. To
identify the point of maximal understanding in an ongoing utterance, we a)
implement an incremental Dialog State Tracker which is updated on a token basis
(iDST) b) re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset and c)
adapt it to the incremental turn-taking experimental scenario. The re-labeling
consists of assigning a binary value to each token in the user utterance that
allows to identify the appropriate point for taking the turn. Finally, we
implement an incremental Turn Taking Decider (iTTD) that is trained on these
new labels for the turn-taking decision. We show that the proposed model can
achieve a better performance compared to a deterministic handcrafted
turn-taking algorithm.
