  Dialogue systems pretrained with large language models generate locally
coherent responses, but lack the fine-grained control over responses necessary
to achieve specific goals. A promising method to control response generation is
exemplar-based generation, in which models edit exemplar responses that are
retrieved from training data, or hand-written to strategically address
discourse-level goals, to fit new dialogue contexts. But, current
exemplar-based approaches often excessively copy words from the exemplar
responses, leading to incoherent replies. We present an Exemplar-based Dialogue
Generation model, EDGE, that uses the semantic frames present in exemplar
responses to guide generation. We show that controlling dialogue generation
based on the semantic frames of exemplars, rather than words in the exemplar
itself, improves the coherence of generated responses, while preserving
semantic meaning and conversation goals present in exemplar responses.
