Practical grammar-based NLG from examples

Abstract

We present a technique that opens up grammar-based generation to a wider range of practical applications by dramatically reducing the development costs and linguistic expertise that are required. Our method infers the grammatical resources needed for generation from a set of declarative examples that link surface expressions directly to the application's available semantic representations. The same examples further serve to optimize a run-time search strategy that generates the best output that can be found within an application-specific time frame. Our method offers substantially lower development costs than hand-crafted grammars for applicationspecific NLG, while maintaining high output quality and diversity.