Learning generation templates from dialog transcripts
First Claim
1. A call center device operating in conjunction with a telephonic or online chat communication station, the call center device comprising:
- a dialog manager configured to determine a recommended agent dialog act based on past dialog between a call center agent and a second party which is received from the telephonic or online chat communication station; and
an utterance generation component configured to generate at least one recommended agent utterance for implementing the recommended dialog act by operations including;
ranking a set of word lattices each represented as a weighted finite state automaton (WFSA) by conditional probabilities p(τ
|DA type) where τ
is a word lattice and DA type is a dialog act type of the recommended dialog act,choosing at least one word lattice from the ranking, andinstantiating the chosen at least one word lattice to generate the at least one recommended agent utterance;
wherein the dialog manager and the utterance generation component comprise at least one computer programmed to determine the recommended dialog act and to generate the at least one recommended agent utterance.
4 Assignments
0 Petitions
Accused Products
Abstract
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.
71 Citations
20 Claims
-
1. A call center device operating in conjunction with a telephonic or online chat communication station, the call center device comprising:
-
a dialog manager configured to determine a recommended agent dialog act based on past dialog between a call center agent and a second party which is received from the telephonic or online chat communication station; and an utterance generation component configured to generate at least one recommended agent utterance for implementing the recommended dialog act by operations including; ranking a set of word lattices each represented as a weighted finite state automaton (WFSA) by conditional probabilities p(τ
|DA type) where τ
is a word lattice and DA type is a dialog act type of the recommended dialog act,choosing at least one word lattice from the ranking, and instantiating the chosen at least one word lattice to generate the at least one recommended agent utterance; wherein the dialog manager and the utterance generation component comprise at least one computer programmed to determine the recommended dialog act and to generate the at least one recommended agent utterance. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
-
-
17. A non-transitory storage medium storing instructions readable and executable by one or more electronic data processing devices to perform a method for generating recommended agent utterances for implementing dialog acts recommended by a dialog manager of a call center, the method comprising:
-
constructing a set of word lattices each represented as a weighted finite state automaton (WFSA) from training dialogs between call center agents and second parties; assigning to the word lattices conditional probabilities over dialog act type; and for each recommended dialog act received from the dialog manager;
(i) ranking the set of word lattices by the conditional probabilities for the dialog act type of the recommended dialog act, (ii) choosing at least one word lattice from the ranking, and (iii) instantiating the chosen at least one word lattice to generate at least one recommended agent utterance for implementing the recommended dialog act. - View Dependent Claims (18, 19, 20)
-
Specification