Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History
Authors: Bowen Wu, WangWenqing, Lihaoran, Yunhan Deng, Ying Li, Jingsong Yu, Baoxun Wang


Reviewer paper summaries: This paper introduces the Memory-aware Proactive Dialogue (MapDia) task to enhance proactive dialogue systems by incorporating long-term memory mechanisms. Traditional proactive dialogue systems often target pre-defined keywords or entities, limiting their ability to develop long-term user intimacy. The authors propose leveraging conversational history to steer dialogues toward relevant historical topics, improving user engagement and service autonomy. They present the first Chinese Memory-aware Proactive Dataset (ChMapData) created through an automatic data construction method. The framework consists of three modules: Topic Summarization, Topic Retrieval, and Proactive Topic-shifting Detection and Generation. Their empirical results show that integrating memory mechanisms significantly enhances dialogue system performance evaluated using automatic and human assessments.
Summary of strengths: 1. The paper introduces a novel task (MapDia) that integrates memory mechanisms into proactive dialogue systems, addressing a significant challenge in enhancing user engagement. 
2. The automatic data construction method for creating ChMapData efficiently generates a robust dataset, reducing the need for extensive human annotation. The release of the ChMapData dataset offers a valuable resource for future research, enhancing reproducibility and practical application.
3. The framework combines Topic Summarization, Topic Retrieval, and Proactive Topic-shifting Detection and Generation, providing a comprehensive approach to leveraging conversational history.
4. Nice experiments and automatic and human evaluations validate the effectiveness of the proposed methods.
Summary of weaknesses: 1. The evaluation is limited to a specific dataset in Chinese, raising questions about the generalizability of the findings to other languages and cultural contexts. 
2. The paper could benefit from a more detailed discussion of potential biases in the dataset and the models used, particularly concerning how these biases might affect the fairness and accuracy of the dialogue systems. 
3. The paper lacks a thorough discussion of the ethical implications of memory-aware dialogue systems, particularly regarding user privacy, data security, and the potential negative influence of referencing history on users with psychological disorders. 
4. Continuously bringing up past adverse events could reinforce negative thought patterns, increase anxiety, or exacerbate symptoms of mental health conditions.
Comments, suggestions and typos: 1. Broaden the evaluation to include datasets from other languages and cultural contexts.
2. Discuss potential biases in the dataset and models and propose methods to identify and mitigate these biases.
3. Provide practical guidance on implementing the framework, including pseudocode or detailed examples.
4. Address the ethical implications of memory-aware dialogue systems, focusing on user privacy, data security, and the potential impact on users with psychological disorders.
Overall assessment scores: 2.5