让聊天机器人记住对话历史，主动引导更自然的对话。
Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History
- 将对话记忆与主动对话结合，实现基于历史的智能引导。
- 构建首个中文记忆感知主动对话数据集（ChMapData）。
- 采用RAG框架，可适时切换话题并保持对话连贯性。
主动对话系统旨在赋予聊天机器人主动引导对话的能力，以提升用户参与度和服务自主性。现有系统通常依赖预定义关键词或实体，忽视对话历史中隐含的用户属性与偏好，限制了长期用户关系的建立。为此，本文提出一个新任务——记忆感知主动对话（MapDia），将主动对话与长期记忆整合至统一框架。通过任务分解，我们设计自动数据构建方法，创建首个中文记忆感知主动对话数据集（ChMapData）。同时提出基于检索增强生成（RAG）的联合框架，包含话题总结、话题检索、主动话题切换检测与生成三个模块，实现对话在恰当时机向相关历史话题引导。自动与人工评估均验证了该数据集与模型的有效性。代码与数据已开源：https://github.com/FrontierLabs/MapDia。
原文摘要 · Abstract (English)
Proactive dialogue systems aim to empower chatbots with the capability of leading conversations towards specific targets, thereby enhancing user engagement and service autonomy. Existing systems typically target pre-defined keywords or entities, neglecting user attributes and preferences implicit in dialogue history, hindering the development of long-term user intimacy. To address these challenges, we take a radical step towards building a more human-like conversational agent by integrating proactive dialogue systems with long-term memory into a unified framework. Specifically, we define a novel task named Memory-aware Proactive Dialogue (MapDia). By decomposing the task, we then propose an automatic data construction method and create the first Chinese Memory-aware Proactive Dataset (ChMapData). Furthermore, we introduce a joint framework based on Retrieval Augmented Generation (RAG), featuring three modules: Topic Summarization, Topic Retrieval, and Proactive Topic-shifting Detection and Generation, designed to steer dialogues towards relevant historical topics at the right time. The effectiveness of our dataset and models is validated through both automatic and human evaluations. We release the open-source framework and dataset at https://github.com/FrontierLabs/MapDia.
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