1. A method for dialog state tracking comprising: learning first and second latent variable models; providing the first and second latent variable models learned by reconstructing a decompositional model in the form of a matrix, 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 state descriptors for the initial and final states each including a value for each of a predefined set of state variables, 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 state and a respective dialogue representation which is based on a user utterance, the state descriptor for the initial state including a value for each of the predefined set of state variables; predicting a state descriptor for the final state of the new dialog state transition using the learned first and second latent variable models, the state descriptor for the final state including a value for each of the predefined set of state variables; and outputting, to a client device, an agent dialog act based on the predicted state descriptor for the final state, and wherein the predicting of the state descriptor is performed with a processor.
1. A method for dialog state tracking comprising: learning first and second latent variable models; providing the first and second latent variable models learned by reconstructing a decompositional model in the form of a matrix, 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 state descriptors for the initial and final states each including a value for each of a predefined set of state variables, 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 state and a respective dialogue representation which is based on a user utterance, the state descriptor for the initial state including a value for each of the predefined set of state variables; predicting a state descriptor for the final state of the new dialog state transition using the learned first and second latent variable models, the state descriptor for the final state including a value for each of the predefined set of state variables; and outputting, to a client device, an agent dialog act based on the predicted state descriptor for the final state, and wherein the predicting of the state descriptor is performed with a processor. 2. The method of claim 1 , wherein the first and second latent variable models are jointly learned.