  Fast expansion of natural language functionality of intelligent virtual
agents is critical for achieving engaging and informative interactions.
However, developing accurate models for new natural language domains is a time
and data intensive process. We propose efficient deep neural network
architectures that maximally re-use available resources through transfer
learning. Our methods are applied for expanding the understanding capabilities
of a popular commercial agent and are evaluated on hundreds of new domains,
designed by internal or external developers. We demonstrate that our proposed
methods significantly increase accuracy in low resource settings and enable
rapid development of accurate models with less data.
