Controlling Dialogue Generation with Semantic Exemplars

Understand

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.