人际记忆至关重要：一项利用对话历史进行主动对话的新任务
Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History
AI总结：
本文提出记忆感知主动对话任务MapDia，构建首个中文数据集ChMapData，并设计基于RAG的三模块联合框架，以在恰当时机引导对话转向相关历史主题。
摘要：
主动对话系统旨在赋予聊天机器人引导对话朝向特定目标的能力，从而提升用户参与度和服务自主性。现有系统通常以预定义的关键词或实体为目标，忽视了对话历史中隐含的用户属性与偏好，阻碍了长期用户亲密关系的发展。为应对这些挑战，我们迈出了激进的一步，通过将主动对话系统与长期记忆整合到一个统一框架中，构建更具人类特征的对话智能体。具体而言，我们定义了一项名为记忆感知主动对话（MapDia）的新任务。通过对该任务进行分解，我们提出了一种自动数据构建方法，并创建了首个中文记忆感知主动对话数据集（ChMapData）。此外，我们引入了一个基于检索增强生成（RAG）的联合框架，包含三个模块：主题摘要、主题检索以及主动主题转移检测与生成，旨在在合适的时机将对话引导至相关的历史主题。我们的数据集和模型的有效性通过自动评估和人工评估得到了验证。我们在 https://github.com/FrontierLabs/MapDia 发布了开源框架和数据集。
英文摘要：
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.
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。
