  In this paper, we explore the task of mapping spoken language utterances to
one of thousands of natural language understanding domains in intelligent
personal digital assistants (IPDAs). This scenario is observed for many
mainstream IPDAs in industry that allow third parties to develop thousands of
new domains to augment built-in ones to rapidly increase domain coverage and
overall IPDA capabilities. We propose a scalable neural model architecture with
a shared encoder, a novel attention mechanism that incorporates personalization
information and domain-specific classifiers that solves the problem
efficiently. Our architecture is designed to efficiently accommodate new
domains that appear in-between full model retraining cycles with a rapid
bootstrapping mechanism two orders of magnitude faster than retraining. We
account for practical constraints in real-time production systems, and design
to minimize memory footprint and runtime latency. We demonstrate that
incorporating personalization results in significantly more accurate domain
classification in the setting with thousands of overlapping domains.
