论文标题
用语义示例控制对话生成
Controlling Dialogue Generation with Semantic Exemplars
论文作者
论文摘要
对对话系统的预测系统会产生本地一致的响应，但缺乏对实现特定目标所需的响应的精细控制。控制响应生成的一种有希望的方法是基于示例性的一代，其中模型编辑了从训练数据检索或手工编写以战略性地解决话语级别目标，以适合新的对话环境的模型。但是，当前基于示例的方法通常会从示例响应中复制单词过多，从而导致不连贯的答复。我们提出了一个基于示例的对话生成模型Edge，该模型使用了示例响应中存在的语义帧来指导生成。我们表明，基于示例的语义框架而不是示例本身中的单词来控制对话生成，可以提高产生的响应的连贯性，同时保留示例响应中存在的语义含义和对话目标。
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.
