Paper: A Joint Model for Discovery of Aspects in Utterances

ACL ID P12-1035
Title A Joint Model for Discovery of Aspects in Utterances
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012

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 do- main (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 un- derstanding model. Using utterances from five domains, our approach shows up to 4.5% im- provement on domain and dialog act perfor- mance over cascaded approach in which each semantic component is learned sequentially and a supervised joint learning model (which requires fully labeled data).