We describe a joint model for understanding
\nuser actions in natural language utterances.
\nOur multi-layer generative approach uses both
\nlabeled and unlabeled utterances to jointly
\nlearn aspects regarding utterance\u2019s target domain
\n(e.g. movies), intention (e.g., finding a
\nmovie) along with other semantic units (e.g.,
\nmovie name). We inject information extracted
\nfrom unstructured web search query logs as
\nprior information to enhance the generative
\nprocess of the natural language utterance understanding
\nmodel. Using utterances from five
\ndomains, our approach shows up to 4.5% improvement
\non domain and dialog act performance
\nover cascaded approach in which each
\nsemantic component is learned sequentially
\nand a supervised joint learning model (which
\nrequires fully labeled data).<\/p>\n","protected":false},"excerpt":{"rendered":"
We describe a joint model for understanding user actions in natural language utterances. Our multi-layer generative approach uses both labeled and unlabeled utterances to jointly learn aspects regarding utterance\u2019s target domain (e.g. movies), intention (e.g., finding a movie) along with other semantic units (e.g., movie name). 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