In this work, we introduce the Keep Emotional and Essential Memory (KEEM) dataset, a novel generation-based dataset designed to enhance memory updates in long-term conversational systems. Unlike existing approaches that rely on simple accumulation or operation-based methods, which often result in information conflicts and difficulties in accurately tracking a user's current state, KEEM dynamically generates integrative memories. This process not only preserves essential factual information but also incorporates emotional context and causal relationships, enabling a more nuanced understanding of user interactions. By seamlessly updating a system's memory with both emotional and essential data, our approach promotes deeper empathy and enhances the system's ability to respond meaningfully in open-domain conversations.

翻译：本研究提出了保持情感与核心记忆（KEEM）数据集，这是一个基于生成方法的新型数据集，旨在提升长话轮对话系统中的记忆更新机制。与现有依赖简单累积或基于操作的方法不同——这些方法常导致信息冲突且难以准确追踪用户当前状态——KEEM能够动态生成整合性记忆。该过程不仅保留了关键事实信息，同时融入了情感语境与因果关系，从而实现对用户交互更细腻的理解。通过将情感数据与核心数据无缝整合至系统记忆更新中，我们的方法能够促进更深层次的共情能力，并增强系统在开放域对话中作出有意义回应的能力。