PRCCF: A Persona-guided Retrieval and Causal-aware Cognitive Filtering Framework for Emotional Support Conversation

Abstract

Emotional Support Conversation (ESC) aims to alleviate individual emotional distress by generating empathetic responses. However, existing methods face challenges in effectively supporting deep contextual understanding. To address this issue, we propose PRCCF, a Persona-guided Retrieval and Causality-aware Cognitive Filtering framework. Specifically, the framework incorporates a persona-guided retrieval mechanism that jointly models semantic compatibility and persona alignment to enhance response generation. Furthermore, it employs a causality-aware cognitive filtering module to prioritize causally relevant external knowledge, thereby improving contextual cognitive understanding for emotional reasoning. Extensive experiments on the ESConv dataset demonstrate that PRCCF outperforms state-of-the-art baselines on both automatic metrics and human evaluations. Our code is publicly available at: https://github.com/YancyLyx/PRCCF.

Figures (6)

• Figure 1: An example illustrating how PRCCF generates emotional support responses. Persona-aligned demonstrations are retrieved to match the user’s expressive style, while causal cues and filtered commonsense knowledge are integrated as cognitive information to guide empathetic response generation.
• Figure 2: The figure illustrates PRCCF framework, consisting of three main modules: the Persona-guided Multi-View Retriever, the Causality-aware Cognitive Filtering, and the Multi-Source Fusion for Generation.
• Figure 3: Top-$n$ accuracy comparison across different methods.
• Figure 4: Effect of the number of retrieved candidate pairs (pairs) in the PR module. All metrics are min--max normalized, with perplexity inverted so that higher values indicate better fluency. Performance peaks at $\textit{pairs}=5$.
• Figure 5: Parameter update dynamics of the Cognitive Knowledge Refinement across training epochs.