DIALGEN: Collaborative Human-LM Generated Dialogues for Improved
  Understanding of Human-Human Conversations
Authors: name: Bo-Ru Lu, name: Nikita Haduong, name: Chia-Hsuan Lee, name: Zeqiu Wu, name: Hao Cheng, name: Paul Koester, name: Jean Utke, name: Tao Yu, name: Noah A. Smith, name: Mari Ostendorf

Applications that could benefit from automatic understanding of human-human
conversations often come with challenges associated with private information in
real-world data such as call center or clinical conversations. Working with
protected data also increases costs of annotation, which limits technology
development. To address these challenges, we propose DIALGEN, a
human-in-the-loop semi-automated dialogue generation framework. DIALGEN uses a
language model (ChatGPT) that can follow schema and style specifications to
produce fluent conversational text, generating a complex conversation through
iteratively generating subdialogues and using human feedback to correct
inconsistencies or redirect the flow. In experiments on structured
summarization of agent-client information gathering calls, framed as dialogue
state tracking, we show that DIALGEN data enables significant improvement in
model performance.