title: DIALGEN: Collaborative Human-LM Generated Dialogues for Improved
    Understanding of Human-Human Conversations
summary: 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.
paper_authors._id: 64b48e161917256b4145b97c
paper_authors.name: Bo-Ru Lu
paper_authors._id: 64b48e161917256b4145b97d
paper_authors.name: Nikita Haduong
paper_authors._id: 64b48e161917256b4145b97e
paper_authors.name: Chia-Hsuan Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-10T08:21:54.989Z
paper_authors._id: 64b5a14a739e4033c79bed96
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64b5a14a739e4033c79bed96/MdcvTKdDknoFlU6Mqhyyt.png
paper_authors.fullname: Chia-Hsuan Lee
paper_authors.type: user
paper_authors.user: chiahsuanlee
paper_authors._id: 64b48e161917256b4145b97f
paper_authors.name: Zeqiu Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-17T10:40:38.726Z
paper_authors._id: 647d335836e109abce3ffb79
paper_authors.avatarUrl: /avatars/fee73f1e15502ca51f8a7a928f653d31.svg
paper_authors.fullname: Zeqiu Wu
paper_authors.type: user
paper_authors.user: zeqiuwu1
paper_authors._id: 64b48e161917256b4145b980
paper_authors.name: Hao Cheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-18T06:44:33.676Z
paper_authors._id: 608c49299d7c9519b4adae25
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/608c49299d7c9519b4adae25/pWSW52_QU7VD9hhDUHbmq.jpeg
paper_authors.fullname: Hao Cheng
paper_authors.type: user
paper_authors.user: kelvinih
paper_authors._id: 64b48e161917256b4145b981
paper_authors.name: Paul Koester
paper_authors._id: 64b48e161917256b4145b982
paper_authors.name: Jean Utke
paper_authors._id: 64b48e161917256b4145b983
paper_authors.name: Tao Yu
paper_authors._id: 64b48e161917256b4145b984
paper_authors.name: Noah A. Smith
paper_authors._id: 64b48e161917256b4145b985
paper_authors.name: Mari Ostendorf
paper_title: DIALGEN: Collaborative Human-LM Generated Dialogues for Improved
    Understanding of Human-Human Conversations
paper_summary: 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.
paper_ai_summary: DIALGEN, a human-in-the-loop framework using ChatGPT, improves structured summarization of agent-client conversations by generating and refining subdialogues with human feedback.
paper_ai_keywords: human-in-the-loop semi-automated dialogue generation language model ChatGPT subdialogues dialogue state tracking