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Many of the creative and figurative elements that make language exciting arelost in translation in current natural language generation engines. In thispaper, we explore a method to harvest templates from positive and negativereviews in the restaurant domain, with the goal of vastly expanding the typesof stylistic variation available to the natural language generator. We learnhyperbolic adjective patterns that are representative of the strongly-valencedexpressive language commonly used in either positive or negative reviews. Wethen identify and delexicalize entities, and use heuristics to extractgeneration templates from review sentences. We evaluate the learned templatesagainst more traditional review templates, using subjective measures of"convincingness", "interestingness", and "naturalness". Our results show thatthe learned templates score highly on these measures. Finally, we analyze thelinguistic categories that characterize the learned positive and negativetemplates. We plan to use the learned templates to improve the conversationalstyle of dialogue systems in the restaurant domain.

Harvesting Creative Templates for Generating Stylistically Varied Restaurant Reviews