DIALGEN: Collaborative Human-LM Generated Dialogues for Improved
  Understanding of Human-Human Conversations
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.
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Bo-Ru Lu
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Nikita Haduong
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Chia-Hsuan Lee
claimed_verified
2024-07-10T08:21:54.989Z
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Chia-Hsuan Lee
user
chiahsuanlee
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Zeqiu Wu
admin_assigned
2023-07-17T10:40:38.726Z
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Zeqiu Wu
user
zeqiuwu1
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Hao Cheng
claimed_verified
2023-07-18T06:44:33.676Z
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Hao Cheng
user
kelvinih
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Paul Koester
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Jean Utke
64b48e161917256b4145b983
Tao Yu
64b48e161917256b4145b984
Noah A. Smith
64b48e161917256b4145b985
Mari Ostendorf
DIALGEN: Collaborative Human-LM Generated Dialogues for Improved
  Understanding of Human-Human Conversations
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.
DIALGEN, a human-in-the-loop framework using ChatGPT, improves structured summarization of agent-client conversations by generating and refining subdialogues with human feedback.
human-in-the-loop semi-automated dialogue generation language model ChatGPT subdialogues dialogue state tracking