1. A method for dialog state tracking comprising: providing first and second latent variable models learned by reconstructing a decompositional model, the decompositional model having been generated from annotated training dialogues and including, for each of a plurality of dialog state transitions, state descriptors for initial and final states of the transition and a respective dialogue representation, the learned first latent variable model including embeddings of the plurality of state transitions, and the learned second latent variable model including embeddings of features of the state descriptors and embeddings of features of the dialogue representations; receiving data for a new dialog state transition, the data including a state descriptor for the initial time and a respective dialogue representation; predicting a state descriptor for the final state of the new dialog state transition using the learned first and second latent variable models, wherein the predicting of the state descriptor is performed with a processor.