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Flowlogue: A Novel Framework for Synthetic Dialogue Generation with Structured Flow from Text Passages

Yongil Kim, Yerin Hwang, Hyunkyung Bae, Taegwan Kang, Kyomin Jung

Abstract: Dialogue systems play a pivotal role in domains ranging from customer service to virtual assistance and education. Integrating Large Language Models (LLMs) has significantly boosted their capabilities, underscoring their potential to facilitate more nuanced human-computer interactions. Despite these advances, a significant challenge persists in curated dialogue data scarcity, especially in Conversational Question Answering (ConvQA) systems that require domain-specific information. Traditional Passage to Dialogue (P2D) methods attempt to mitigate this by converting textual passages into dialogue form but often struggle with issues such as unnatural responses and information redundancy. To overcome these limitations, we introduce Flowlogue, a novel ConvQA framework that enhances dialogue generation by merging related sentences within passages to maintain natural flow and coherence. Our experimental results confirm that Flowlogue produces superior dialogues, establishing a robust framework for generating natural, high-quality ConvQA dialogues.