Also, modeling the high-level decision-making policy in dialogs enables useful generalization and dataefficient domain adaptation (Gašić et al., 2010).
the motivation of this paper is to develop an unsupervised neural recognition model that can discover interpretable meaning representations of utterances (denoted as latent actions) as a set of discrete latent variables from a large unlabelled corpus as shown in Figure 1.