  Intelligent personal digital assistants (IPDAs), a popular real-life
application with spoken language understanding capabilities, can cover
potentially thousands of overlapping domains for natural language
understanding, and the task of finding the best domain to handle an utterance
becomes a challenging problem on a large scale. In this paper, we propose a set
of efficient and scalable neural shortlisting-reranking models for large-scale
domain classification in IPDAs. The shortlisting stage focuses on efficiently
trimming all domains down to a list of k-best candidate domains, and the
reranking stage performs a list-wise reranking of the initial k-best domains
with additional contextual information. We show the effectiveness of our
approach with extensive experiments on 1,500 IPDA domains.
