  Dialogue policies play a crucial role in developing task-oriented dialogue
systems, yet their development and maintenance are challenging and typically
require substantial effort from experts in dialogue modeling. While in many
situations, large amounts of conversational data are available for the task at
hand, people lack an effective solution able to extract dialogue policies from
this data. In this paper, we address this gap by first illustrating how Large
Language Models (LLMs) can be instrumental in extracting dialogue policies from
datasets, through the conversion of conversations into a unified intermediate
representation consisting of canonical forms. We then propose a novel method
for generating dialogue policies utilizing a controllable and interpretable
graph-based methodology. By combining canonical forms across conversations into
a flow network, we find that running graph traversal algorithms helps in
extracting dialogue flows. These flows are a better representation of the
underlying interactions than flows extracted by prompting LLMs. Our technique
focuses on giving conversation designers greater control, offering a
productivity tool to improve the process of developing dialogue policies.
