Intent detectors in state-of-the-art spoken language understanding
\nsystems are often trained with a small number of manually annotated
\nexamples collected from the application domain. Search query
\nlogs provide a large number of unlabeled queries that would be beneficial
\nto improve such supervised classification. Furthermore, the
\ncontents of user queries as well as the clicked URLs provide information
\nabout user\u2019s intent. In this paper, we propose a variational
\nBayesian approach for modeling latent intents of user queries and
\nclicked URLs when available. We use this model to enhance supervised
\nintent classification of user queries from conversational interactions.
\nExperiments were run with large volumes of search queries
\nand show significant improvements over state-of-the-art systems.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"IEEE Intl. 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