The prevalence of mental disorders has become a significant issue, leading to the increased focus on Emotional Support Conversation as an effective supplement for mental health support. Existing methods have achieved compelling results, however, they still face three challenges: 1) variability of emotions, 2) practicality of the response, and 3) intricate strategy modeling. To address these challenges, we propose a novel knowledge-enhanced Memory mODEl for emotional suppoRt coNversation (MODERN). Specifically, we first devise a knowledge-enriched dialogue context encoding to perceive the dynamic emotion change of different periods of the conversation for coherent user state modeling and select context-related concepts from ConceptNet for practical response generation. Thereafter, we implement a novel memory-enhanced strategy modeling module to model the semantic patterns behind the strategy categories. Extensive experiments on a widely used large-scale dataset verify the superiority of our model over cutting-edge baselines.

翻译：心理健康问题的普遍性已成为一个重大问题，促使情感支持对话作为心理健康支持的有效补充手段受到越来越多的关注。现有方法已取得显著成果，但仍面临三个挑战：1）情绪多变性，2）回应实用性，以及3）复杂策略建模。为解决这些问题，我们提出了一种新颖的知识增强记忆模型（MODERN）用于情感支持对话。具体而言，我们首先设计了一种知识增强的对话上下文编码，以感知对话不同阶段的动态情绪变化，实现连贯的用户状态建模，并从ConceptNet中选择上下文相关概念以生成实用回应。随后，我们实现了一种新颖的记忆增强策略建模模块，用于建模策略类别背后的语义模式。在广泛使用的大规模数据集上进行的大量实验验证了我们的模型相较于前沿基线的优越性。