System and Method of Semi-Supervised Learning for Spoken Language Understanding Using Semantic Role Labeling

A system comprising:

• a processor;
• and
• a computer-readable storage medium having instructions stored which, when executed by the processor, perform a method comprising:
• selecting an intent from a list of predicate/argument pairs associated with a spoken dialog system;
• labeling training data using mapping rules associated with the intent, wherein the mapping rules specify rules for selecting a call-type label for an utterance;
• and
• while the training data and a classification model associated with the call-type label have a divergence above a threshold, iteratively:
• training the classification model using the training data;
• and
• re-labeling the training data using the classification model.

A computer-readable storage medium having instructions stored which, when executed by a computing device, cause the computing device to perform a method comprising:

• selecting an intent from a list of predicate/argument pairs associated with a spoken dialog system;
• labeling training data using mapping rules associated with the intent, wherein the mapping rules specify rules for selecting a call-type label for an utterance;
• and
• while the training data and a classification model associated with the call-type label have a divergence above a threshold, iteratively:
• training the classification model using the training data;
• and
• re-labeling the training data using the classification model.