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Fast expansion of natural language functionality of intelligent virtualagents is critical for achieving engaging and informative interactions.However, developing accurate models for new natural language domains is a timeand data intensive process. We propose efficient deep neural networkarchitectures that maximally re-use available resources through transferlearning. Our methods are applied for expanding the understanding capabilitiesof a popular commercial agent and are evaluated on hundreds of new domains,designed by internal or external developers. We demonstrate that our proposedmethods significantly increase accuracy in low resource settings and enablerapid development of accurate models with less data.

Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents