  Domain classification is the task of mapping spoken language utterances to
one of the natural language understanding domains in intelligent personal
digital assistants (IPDAs). This is a major component in mainstream IPDAs in
industry. Apart from official domains, thousands of third-party domains are
also created by external developers to enhance the capability of IPDAs. As more
domains are developed rapidly, the question of how to continuously accommodate
the new domains still remains challenging. Moreover, existing continual
learning approaches do not address the problem of incorporating personalized
information dynamically for better domain classification. In this paper, we
propose CoNDA, a neural network based approach for domain classification that
supports incremental learning of new classes. Empirical evaluation shows that
CoNDA achieves high accuracy and outperforms baselines by a large margin on
both incrementally added new domains and existing domains.
