Recently introduced dialogue systems have demonstrated high usability. However, they still fall short of reflecting real-world conversation scenarios. Current dialogue systems exhibit an inability to replicate the dynamic, continuous, long-term interactions involving multiple partners. This shortfall arises because there have been limited efforts to account for both aspects of real-world dialogues: deeply layered interactions over the long-term dialogue and widely expanded conversation networks involving multiple participants. As the effort to incorporate these aspects combined, we introduce Mixed-Session Conversation, a dialogue system designed to construct conversations with various partners in a multi-session dialogue setup. We propose a new dataset called MiSC to implement this system. The dialogue episodes of MiSC consist of 6 consecutive sessions, with four speakers (one main speaker and three partners) appearing in each episode. Also, we propose a new dialogue model with a novel memory management mechanism, called Egocentric Memory Enhanced Mixed-Session Conversation Agent (EMMA). EMMA collects and retains memories from the main speaker's perspective during conversations with partners, enabling seamless continuity in subsequent interactions. Extensive human evaluations validate that the dialogues in MiSC demonstrate a seamless conversational flow, even when conversation partners change in each session. EMMA trained with MiSC is also evaluated to maintain high memorability without contradiction throughout the entire conversation.

翻译：近期引入的对话系统已展现出较高的可用性，然而它们仍难以反映真实世界的对话场景。当前的对话系统无法复现涉及多方参与者的动态、连续、长期交互。这一不足源于现有研究对真实世界对话的两个方面关注有限：长期对话中深度分层的交互结构，以及涉及多参与者的广泛扩展对话网络。为综合纳入这些方面，我们提出混合会话对话系统，该系统旨在多会话对话设置中构建与不同伙伴的对话。为实现该系统，我们提出了名为MiSC的新数据集。MiSC的对话片段由6个连续会话构成，每个片段包含四位说话者（一位主说话者和三位对话伙伴）。此外，我们提出了一种采用新型记忆管理机制的对话模型——自我中心记忆增强型混合会话对话代理（EMMA）。EMMA在与伙伴对话过程中，从主说话者视角收集并保留记忆，从而在后续交互中实现无缝连续性。大量人工评估证实，即使每个会话的对话伙伴发生变化，MiSC中的对话仍能呈现连贯的对话流。使用MiSC训练的EMMA经评估表明，其在整个对话过程中能保持高记忆一致性且无矛盾。