A Variational Bayesian Model for User Intent Detection

Intent detectors in state-of-the-art spoken language understanding
systems are often trained with a small number of manually annotated
examples collected from the application domain. Search query
logs provide a large number of unlabeled queries that would be beneficial
to improve such supervised classification. Furthermore, the
contents of user queries as well as the clicked URLs provide information
about user’s intent. In this paper, we propose a variational
Bayesian approach for modeling latent intents of user queries and
clicked URLs when available. We use this model to enhance supervised
intent classification of user queries from conversational interactions.
Experiments were run with large volumes of search queries
and show significant improvements over state-of-the-art systems.