Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History

ACL ARR 2024 December Submission766 Authors

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.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: Proactive Dialogue, Dialogue System, Long-term Memory, Topic shift, Dataset

Contribution Types: Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English, Chinese

Submission Number: 766