  Many of the creative and figurative elements that make language exciting are
lost in translation in current natural language generation engines. In this
paper, we explore a method to harvest templates from positive and negative
reviews in the restaurant domain, with the goal of vastly expanding the types
of stylistic variation available to the natural language generator. We learn
hyperbolic adjective patterns that are representative of the strongly-valenced
expressive language commonly used in either positive or negative reviews. We
then identify and delexicalize entities, and use heuristics to extract
generation templates from review sentences. We evaluate the learned templates
against more traditional review templates, using subjective measures of
"convincingness", "interestingness", and "naturalness". Our results show that
the learned templates score highly on these measures. Finally, we analyze the
linguistic categories that characterize the learned positive and negative
templates. We plan to use the learned templates to improve the conversational
style of dialogue systems in the restaurant domain.
