  This paper introduces a new unsupervised approach for dialogue act induction.
Given the sequence of dialogue utterances, the task is to assign them the
labels representing their function in the dialogue.
  Utterances are represented as real-valued vectors encoding their meaning. We
model the dialogue as Hidden Markov model with emission probabilities estimated
by Gaussian mixtures. We use Gibbs sampling for posterior inference.
  We present the results on the standard Switchboard-DAMSL corpus. Our
algorithm achieves promising results compared with strong supervised baselines
and outperforms other unsupervised algorithms.
