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.