US Patent 9473637 Learning generation templates from dialog transcripts

Agent utterances are generated for implementing dialog acts recommended by a dialog manager of a call center. To this end, a set of word lattices, each represented as a weighted finite state automaton (WFSA), is constructed from training dialogs between call center agents and second parties (e.g. customers). The word lattices are assigned conditional probabilities over dialog act type. For each dialog act received from the dialog manager, the word lattices are ranked by the conditional probabilities for the dialog act type. At least one word lattice is chosen from the ranking, and is instantiated to generate a recommended agent utterance for implementing the recommended dialog act. The word lattices may be constructed by clustering agent utterances of training dialogs using context features from preceding second party utterances and grammatical dependency link features between words within agent utterances. Path variations of the word lattices may define slots or paraphrases.