Social chatbots have become essential companions in daily scenarios ranging from emotional support to personal interaction. However, conventional chatbots with passive response mechanisms usually rely on users to initiate or sustain dialogues by bringing up new topics, resulting in diminished engagement and shortened dialogue duration. In this paper, we present PaRT, a novel framework enabling context-aware proactive dialogues for social chatbots through personalized real-time retrieval and generation. Specifically, PaRT first integrates user profiles and dialogue context into a large language model (LLM), which is initially prompted to refine user queries and recognize underlying intents for the upcoming conversation. Guided by refined intents, the LLM generates personalized dialogue topics as targeted queries to retrieve relevant passages from RedNote. Finally, we prompt LLMs with summarized passages to generate knowledge-grounded and engagement-optimized responses. Our approach has been running stably in a real-world production environment for more than 30 days, achieving a 21.77% improvement in the average duration of dialogues.

Document-level relation extraction (DocRE)involves identifying relations between entities distributed in multiple sentences within a document.Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities.However, there are two drawbacks in existing methods.On one hand, anaphor plays an important role in reasoning to identify relations between entities but is ignored by these methods.On the other hand, these methods achieve crosssentence entity interactions implicitly by utilizing a document or sentences as intermediate nodes.Such an approach has difficulties in learning fine-grained interactions between entities across different sentences, resulting in sub-optimal performance.To address these issues, we propose an Anaphor-Assisted (AA) framework for DocRE tasks.Experimental results on the widely-used datasets demonstrate that our model achieves a new state-of-the-art performance.1