  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.

Computation and Language;Artificial Intelligence;Machine Learning