Templated rule-based data augmentation for intent extraction

A method of operating an agent automation system, comprising:

• generalizing an understanding model by expanding meaning representations of the understanding model based on a generalizing rule-set;
• refining the understanding model by focusing the meaning representations of the understanding model based on a refining rule-set;
• searching the understanding model for a match to at least one meaning representation of a received user utterance to extract intents/entities from the received user utterance;
• and
• performing one or more actions in response to the intents/entities.

An agent automation system, comprising:

• a memory configured to store a natural language understanding (NLU) framework, a model augmentation template, and an understanding model, wherein the understanding model includes meaning representations for sample utterances of an intent/entity model;
• and
• a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions comprising:
• generalizing the understanding model by selectively expanding the meaning representations of the understanding model based on a generalizing rule-set of the model augmentation template;
• refining the understanding model by selectively modifying one or more of the meaning representations of the understanding model based on a refining rule-set of the model augmentation template;
• and
• searching the meaning representations of the understanding model to identify intents/entities from a received user utterance.