International Business Machines Corporation
Driving intent expansion via anomaly detection in a modular conversational system

Abstract:

Embodiments provide for driving intent expansion via anomaly detection by ranking, according to anomaly scores, a plurality of historic utterances that have been associated by a classifier with a given intent of a plurality of predefined intents; identifying a given utterance from the plurality of historic utterances having a given anomaly score greater than an anomaly threshold; in response to verifying that the given utterance is associated with the given intent, adding the given utterance to a training dataset as a positive example for the given intent; and in response to verifying that the given utterance is not associated with the given intent, adding the given utterance to the training dataset as a complement example for the given intent. A complement example for one intent may be added as a positive example for a different intent. The training dataset may be used to train or retrain an intent classifier.