Yangfeng Ji, Dilek Hakkani-Tur, Asli Celikyilmaz, Larry Heck, and Gokhan Tur
State-of-the art spoken language understanding models that automatically capture user intents in human to machine dialogs 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 URLs they click provide information about user’s intent. In this paper, we propose a variational Bayesian
approach for modeling latent intents of user queries and URLs that they clicked on when available. We use this model to enhance supervised intent classification of user queries from conversational interactions. Our experimental results demonstrate the effectiveness of
this approach, showing further improvements when a large number of search queries are used.
|Publisher||IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)|