通过人格引导与因果认知过滤，提升情感支持对话的共情理解能力
PRCCF: A Persona-guided Retrieval and Causal-aware Cognitive Filtering Framework for Emotional Support Conversation
- 基于人格匹配与语义兼容性检索响应
- 利用因果关联筛选外部知识，增强情绪推理能力
- 在ESConv数据集上优于现有方法，适合心理辅助场景
情感支持对话（ESC）旨在通过生成共情回应缓解个体情绪困扰。现有方法在深层上下文理解方面仍存挑战。为此，我们提出PRCCF：一种人格引导检索与因果感知认知过滤框架。该框架包含人格引导检索机制，联合建模语义兼容性与人格一致性以优化回应生成；同时引入因果感知认知过滤模块，优先选择具有因果关联的外部知识，从而提升上下文认知理解能力。在ESConv数据集上的大量实验表明，PRCCF在自动指标与人工评估上均优于当前最优基线。代码已公开于https://github.com/YancyLyx/PRCCF。
原文摘要 · Abstract (English)
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.
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