Robust Natural Language Generation from Large-Scale Knowledge Bases

We have begun to see the emergence of large-scale knowledge bases that house tens of thousands of facts encoded in expressive representational languages. The richness of these representations o er the promise of signi cantly improving the quality of natural language generation, but their representational 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 speci cations 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 speci cation, 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, e cient, expressive, and appropriate for a broad range of applications.