Generative discriminative approach for transactional dialog state tracking via collective matrix factorization
First Claim
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.
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Abstract
A computer-implemented method for dialog state tracking employs first and second latent variable models which have been learned by reconstructing a decompositional model generated from annotated training dialogs. The decompositional model includes, for each of a plurality of dialog state transitions corresponding to a respective turn of one of the training dialogs, state descriptors for initial and final states of the transition and a respective representation of the dialog for that turn. The first latent variable model includes embeddings of the plurality of state transitions, and the second latent variable model includes embeddings of features of the state descriptors and embeddings of features of the dialog representations. Data for a new dialog state transition is received, including a state descriptor for the initial time and a respective dialog representation. A state descriptor for the final state of the new dialog state transition is predicted using the learned latent variable models.
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Citations
16 Claims
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1. A method for dialog state tracking comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A system for dialog state tracking comprising:
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a learning component for learning first and second latent variable models; memory which stores the first and second latent variable models, each in the form of a matrix, that have been 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 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; an information gathering component which, during a dialog, receives an utterance of a user for each of a plurality of new dialog state transitions; a representation generation component which generates a dialog representation based on the user utterance; a prediction component which predicts a state descriptor for a final state of each new dialog state transition using the learned first and second latent variable models, an initial state descriptor and the respective dialog representation; an execution component which executes a task associated with one of the predicted final state descriptors; and a processor which implements the information gathering component, representation generation component, and the prediction component.
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16. A method for identifying a transaction comprising:
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learning first and second latent variable models for 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; for each of a plurality of turns of a dialogue; receiving data for a new dialog state transition, the data including a state descriptor for the initial state 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, including computing an embedding of the new dialog state transition in the first latent variable model and computing a distribution over the state as a vector-matrix product between the embedding and the second latent variable model; generating an agent dialog act based on the predicted state descriptor; and implementing a transaction identified based on the predicted final state of at least one of the turns of the dialogue.
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Specification