Despite advancements in on-topic dialogue systems, effectively managing topic
shifts within dialogues remains a persistent challenge, largely attributed to
the limited availability of training datasets. To address this issue, we
propose Multi-Passage to Dialogue (MP2D), a data generation framework that
automatically creates conversational question-answering datasets with natural
topic transitions. By leveraging the relationships between entities in a
knowledge graph, MP2D maps the flow of topics within a dialogue, effectively
mirroring the dynamics of human conversation. It retrieves relevant passages
corresponding to the topics and transforms them into dialogues through the
passage-to-dialogue method. Through quantitative and qualitative experiments,
we demonstrate MP2D's efficacy in generating dialogue with natural topic
shifts. Furthermore, this study introduces a novel benchmark for topic shift
dialogues, TS-WikiDialog. Utilizing the dataset, we demonstrate that even Large
Language Models (LLMs) struggle to handle topic shifts in dialogue effectively,
and we showcase the performance improvements of models trained on datasets
generated by MP2D across diverse topic shift dialogue tasks.

虽然在话题对话系统方面取得了很大进展，但如何有效地在对话中处理话题转换仍然是一个持久的挑战。本研究提出了 Multi-Passage to Dialogue (MP2D) 框架，该框架通过自动生成自然话题转换的对话问答数据集来解决这个问题。通过利用知识图谱中实体之间的关系，MP2D 能够映射对话中的话题流程，有效地模拟人类对话的动态过程。它通过将与话题相关的段落转换为对话形式，将其转化为对话。通过定量和定性实验证明了 MP2D 在生成具有自然话题转换的对话方面的有效性。此外，本研究还引入了一个新的话题转换对话基准，TS-WikiDialog。通过利用该数据集，我们展示了即使是大型语言模型（LLMs）也难以有效处理对话中的话题转换，并展示了使用 MP2D 生成的数据集训练模型在不同话题转换对话任务中的性能提升。