Automated dialogue systems are important applications of artificial
intelligence, and traditional systems struggle to understand user emotions and
provide empathetic feedback. This study integrates emotional intelligence
technology into automated dialogue systems and creates a dialogue generation
model with emotional intelligence through deep learning and natural language
processing techniques. The model can detect and understand a wide range of
emotions and specific pain signals in real time, enabling the system to provide
empathetic interaction. By integrating the results of the study "Can artificial
intelligence detect pain and express pain empathy?", the model's ability to
understand the subtle elements of pain empathy has been enhanced, setting
higher standards for emotional intelligence dialogue systems. The project aims
to provide theoretical understanding and practical suggestions to integrate
advanced emotional intelligence capabilities into dialogue systems, thereby
improving user experience and interaction quality.

该研究将情感智能技术整合进自动对话系统中，通过深度学习和自然语言处理技术创建了一个具有情感智能的对话生成模型。该模型能够实时检测和理解广泛的情绪和特定的疼痛信号，使系统能够提供共情互动。通过整合研究 “人工智能能检测疼痛并表达痛苦共情吗？” 的结果，增强了模型理解痛苦共情的微妙元素的能力，为情感智能对话系统设定了更高的标准。该项目旨在为将先进的情感智能能力整合到对话系统中提供理论理解和实际建议，从而提高用户体验和交互质量。

基于自动对话系统的情感智能对话生成研究

Research on emotionally intelligent dialogue generation based on  automatic dialogue system

Health coaching helps patients achieve personalized and lifestyle-related
goals, effectively managing chronic conditions and alleviating mental health
issues. It is particularly beneficial, however cost-prohibitive, for
low-socioeconomic status populations due to its highly personalized and
labor-intensive nature. In this paper, we propose a neuro-symbolic goal
summarizer to support health coaches in keeping track of the goals and a
text-units-text dialogue generation model that converses with patients and
helps them create and accomplish specific goals for physical activities. Our
models outperform previous state-of-the-art while eliminating the need for
predefined schema and corresponding annotation. We also propose a new health
coaching dataset extending previous work and a metric to measure the
unconventionality of the patient's response based on data difficulty,
facilitating potential coach alerts during deployment.

我们提出了一种神经符号化目标总结器来支持健康教练跟踪目标，以及一种与患者对话并帮助他们创建和完成特定的体育活动目标的文本单元 - 文本对话生成模型。我们的模型在消除了预定义模式和相应标注的同时，胜过了先前的最先进模型。我们还提出了一个扩展之前工作的新的健康教练数据集，并提出了一个基于数据难度衡量患者回应的不常规性的度量方法，以便在部署期间潜在地提醒教练。