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's target domain (e.g. movies), intention (e.g., finding a movie) along with other semantic units (e.g., movie name). We inject information extracted from unstructured web search query logs as prior information to enhance the generative process of the natural language utterance understanding model. Using utterances from five domains, our approach shows up to 4.5% improvement on domain and dialog act performance over cascaded approach in which each semantic component is learned sequentially and a supervised joint learning model (which requires fully labeled data).
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