具有自我中心记忆的混合会话对话
Mixed-Session Conversation with Egocentric Memory
AI总结：
本文提出混合会话对话系统及MiSC数据集，并设计基于自我中心记忆的EMMA模型，以支持多伙伴、多会话的长期连续对话。
摘要：
近期推出的对话系统已展现出较高的可用性，但它们仍不足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个对话伙伴的动态、连续、长期交互。这一不足源于现有工作在同时考虑真实世界对话的两个方面上投入有限：长期对话中深度分层的交互，以及涉及多个参与者的广泛扩展的对话网络。为了将这两个方面结合起来，我们引入了混合会话对话（Mixed-Session Conversation），这是一个旨在多会话对话设置中与不同伙伴构建对话的对话系统。我们提出了一个名为MiSC的新数据集来实现该系统。MiSC的对话片段由6个连续会话组成，每个片段中出现四位说话者（一位主说话者和三位伙伴）。此外，我们提出了一种具有新颖记忆管理机制的对话模型，称为自我中心记忆增强的混合会话对话智能体（EMMA）。EMMA在与伙伴对话期间从主说话者的视角收集并保留记忆，从而在后续交互中实现无缝连续性。广泛的人工评估验证了MiSC中的对话即使在每个会话中对话伙伴发生变化时也能展现出流畅的对话衔接。同时，使用MiSC训练的EMMA也被评估为能够在整个对话过程中保持高记忆性且不产生矛盾。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
