GENERATIVE / DISCRIMINATORY APPROACH TO MONITORING TRANSACTIONAL DIALOGUE STATES BY COLLECTIVE MATRIX FACTORIZATION

摘要

A state dialog tracking method employs first and second latent variable models that have been learned by reconstructing a decompositional model generated from annotated learning dialogues. The decompositional model includes, for each of a plurality of dialog state transitions corresponding to a respective turn of one of the training conversations, state descriptors for the initial and final states of the transition and a representation. of the conversation for that turn. The first latent variable model includes embeddings of the plurality of state transitions, and the second latent variable model includes feature embeddings of the state descriptors and feature embeddings of the speech representations. Data is received for a new state dialog transition that includes a state descriptor for the initial time and a respective conversation representation. A state descriptor for the final state of the new state dialog transition is predicted using the learned latent variable models.