Unsupervised modeling of user actions in a dialog corpus

In data-driven spoken dialog system development, developers should prepare a dialog corpus with semantic annotation. However, the labeling process is a laborious and time consuming task. To reduce human efforts, we propose an unsupervised approach based on non-parametric Bayesian Hidden Markov Model to the problem of modeling user actions. With the non-parametric model, system designers do not need to determine the number and type of user actions. In the experiments, we evaluated the clustering results by comparing them to the human annotation. We also tested a dialog system that used models trained from the automatically annotated corpus with a user simulation.