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In the field of natural language processing, open-domain chatbots haveemerged as an important research topic. However, a major limitation of existingopen-domain chatbot research is its singular focus on short single-sessiondialogue, neglecting the potential need for understanding contextualinformation in multiple consecutive sessions that precede an ongoing dialogue.Among the elements that compose the context in multi-session conversationsettings, the time intervals between sessions and the relationships betweenspeakers would be particularly important. Despite their importance, currentresearch efforts have not sufficiently addressed these dialogical components.In this paper, we introduce a new 1M multi-session dialogue dataset, calledConversation Chronicles, for implementing a long-term conversation setup inwhich time intervals and fine-grained speaker relationships are incorporated.Following recent works, we exploit a large language model to produce the data.The extensive human evaluation shows that dialogue episodes in ConversationChronicles reflect those properties while maintaining coherent and consistentinteractions across all the sessions. We also propose a dialogue model, calledReBot, which consists of chronological summarization and dialogue generationmodules using only around 630M parameters. When trained on ConversationChronicles, ReBot demonstrates long-term context understanding with a highhuman engagement score.

Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations