知识增强的记忆模型用于情感支持对话
Knowledge-enhanced Memory Model for Emotional Support Conversation
AI总结：
针对情感支持对话中情绪多变、回应不实用和策略建模复杂的问题，提出知识增强记忆模型MODERN，通过知识丰富的上下文编码和记忆增强策略建模，在大规模数据集上超越现有基线。
摘要：
心理障碍的普遍性已成为一个重大问题，导致情感支持对话作为心理健康支持的有效补充而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）情绪的变异性，2）回应的实用性，以及3）复杂的策略建模。为应对这些挑战，我们提出了一种新颖的知识增强的记忆模型用于情感支持对话（MODERN）。具体而言，我们首先设计了一种知识丰富的对话上下文编码，以感知对话不同阶段的动态情绪变化，从而实现连贯的用户状态建模，并从ConceptNet中选择与上下文相关的概念以生成实用的回应。随后，我们实现了一种新颖的记忆增强策略建模模块，以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对于最先进基线的优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
