Structured Dialogue Refinement: Building Retrieval-Augmented Question Answering on Goal-Oriented Dialogues

摘要

Retrieval-Augmented Generation (RAG) is widely used for question answering over well-structured document corpora. However, a large amount of real-world problem-solving knowledge is captured in goal-oriented dialogues, where common ground misalignment between users and helpers gives rise to sparse, diffuse, and dynamically refined evidence that challenges standard RAG pipelines. We propose Structured Dialogue Refinement (SDR), a unified framework that adapts dialogue corpora for RAG at both the retrieval and generation stages without altering the underlying pipeline. Specifically, SDR introduces Dual Dialogue Querying for intent-aligned retrieval via issue-centric and solution-centric pseudo-documents, and Graph-Structured Dialogues coupled with a relevance-driven subgraph selection strategy to enable effective utilization of conversational evidence. We further adopt a nugget-based evaluation setup for dialogue-grounded RAG, enabling fine-grained analysis of retrieval coverage and grounded answer generation. Experiments demonstrate that SDR substantially improves both retrieval quality and grounded QA performance under dialogue-specific structural challenges.