  One of the primary tasks in Natural Language Understanding (NLU) is to
recognize the intents as well as domains of users' spoken and written language
utterances. Most existing research formulates this as a supervised
classification problem with a closed-world assumption, i.e. the domains or
intents to be identified are pre-defined or known beforehand. Real-world
applications however increasingly encounter dynamic, rapidly evolving
environments with newly emerging intents and domains, about which no
information is known during model training. We propose a novel framework,
ADVIN, to automatically discover novel domains and intents from large volumes
of unlabeled data. We first employ an open classification model to identify all
utterances potentially consisting of a novel intent. Next, we build a knowledge
transfer component with a pairwise margin loss function. It learns
discriminative deep features to group together utterances and discover multiple
latent intent categories within them in an unsupervised manner. We finally
hierarchically link mutually related intents into domains, forming an
intent-domain taxonomy. ADVIN significantly outperforms baselines on three
benchmark datasets, and real user utterances from a commercial voice-powered
agent.
