构建情感与事实融合的记忆数据集，提升对话系统长期记忆能力
Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation
- 基于生成式方法动态整合用户对话中的情感与关键信息
- 支持系统持续更新记忆，避免信息冲突并准确追踪用户状态
- 适合研究长期对话、情感计算和个性化智能助手的开发者
本文提出KEEM数据集，一种基于生成式的新型长程对话记忆更新数据集。不同于传统简单累积或操作式方法导致的信息冲突与用户状态追踪困难，KEEM通过动态生成整合性记忆，既保留关键事实，又融入情感上下文与因果关系，实现对用户交互更细腻的理解。该方法使系统能无缝更新包含情感与核心信息的记忆，促进深层共情，显著增强开放域对话中的回应质量与连贯性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。