Grounded dialogue models generate responses that are grounded on certain concepts. Limited by the distribution of grounded dialogue data, models trained on such data face the transferability challenges in terms of the data distribution and the type of grounded concepts. To address the challenges, we propose the grounded minimal editing framework, which minimally edits existing responses to be grounded on the given concept. Focusing on personas, we propose Grounded Minimal Editor (GME), which learns to edit by disentangling and recombining persona-related and persona-agnostic parts of the response. To evaluate persona-grounded minimal editing, we present the PersonaMinEdit dataset, and experimental results show that GME outperforms competitive baselines by a large margin. To evaluate the transferability, we experiment on the test set of BlendedSkillTalk and show that GME can edit dialogue models' responses to largely improve their persona consistency while preserving the use of knowledge and empathy.

提出基于概念的实体对话模型面临数据分布和概念类型转移问题的研究。针对这些问题，本文通过提出最小化编辑框架，学习如何基于给定的概念对现有响应进行最小化编辑。通过对话人物的实验，提出了Grounded Minimal Editor（GME）来学习如何编辑有关对话人物的响应，并提出了PersonaMinEdit数据集来评估人物相关的最小编辑。实验结果显示，GME在保留知识和同理心的同时，可以显著提高对话模型的人物一致性，且在传递性上有较好的表现。

通过基于人格和事实最小修改实现可转移的对话