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• W1514579366 abstract "We have begun to see the emergence oflarge-scale knowledge bases that house tens of thousands of facts encoded inexpressive representational la guages. Therichness ofthese representations offer the promise ofsignificantly improving the quality ofnatural language neration, buttheir epresentational complexity, scale, and task-independence pose great challenges to generators. We have designed, implemented, and empirically evaluated FARE, a functional realization system that exploits message specifications drawn from large-scale knowledge bases to create functional descriptions, which are expressions that encode both functional information (case assignment) and structural information (phrasal constituent embeddings). Given a message specification, FARE exploits lexical and grammatical annotations on knowledge base objects to construct functional descriptions, which are then converted to text by a surface generator. Two empirical studies--one with an explanation generator and one with a qualitative model builder--suggest that FARE is robust, efficient, expressive, and appropriate for a broad range of applications." @default.
• W1514579366 created "2016-06-24" @default.
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• W1514579366 date "2001-01-01" @default.
• W1514579366 modified "2023-09-27" @default.
• W1514579366 title "Robust Natural Language Generation from Large-Scale Knowledge Bases 1" @default.
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