A Joint Model for Discovery of Aspects in Utterances


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… (More)

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