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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History
%A Wu, Bowen
%A Wang, Wenqing
%A Li, Haoran
%A Deng, Yunhan
%A Li, Ying
%A Yu, Jingsong
%A Wang, Baoxun
%Y Boleda, Gemma
%Y Roth, Michael
%S Proceedings of the 29th Conference on Computational Natural Language Learning
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-271-8
%F wu-etal-2025-interpersonal
%X 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.
%R 10.18653/v1/2025.conll-1.4
%U https://aclanthology.org/2025.conll-1.4/
%U https://doi.org/10.18653/v1/2025.conll-1.4
%P 47-67

Markdown (Informal)

[Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History](https://aclanthology.org/2025.conll-1.4/) (Wu et al., CoNLL 2025)

• Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History (Wu et al., CoNLL 2025)

ACL

• Bowen Wu, Wenqing Wang, Haoran Li, Yunhan Deng, Ying Li, Jingsong Yu, and Baoxun Wang. 2025. Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History. In Proceedings of the 29th Conference on Computational Natural Language Learning, pages 47–67, Vienna, Austria. Association for Computational Linguistics.