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

Developing intelligent persuasive conversational agents to change people's
opinions and actions for social good is the frontier in advancing the ethical
development of automated dialogue systems. To do so, the first step is to
understand the intricate organization of strategic disclosures and appeals
employed in human persuasion conversations. We designed an online persuasion
task where one participant was asked to persuade the other to donate to a
specific charity. We collected a large dataset with 1,017 dialogues and
annotated emerging persuasion strategies from a subset. Based on the
annotation, we built a baseline classifier with context information and
sentence-level features to predict the 10 persuasion strategies used in the
corpus. Furthermore, to develop an understanding of personalized persuasion
processes, we analyzed the relationships between individuals' demographic and
psychological backgrounds including personality, morality, value systems, and
their willingness for donation. Then, we analyzed which types of persuasion
strategies led to a greater amount of donation depending on the individuals'
personal backgrounds. This work lays the ground for developing a personalized
persuasive dialogue system.

本文旨在开发个性化的智能说服对话系统来改变人们的观点和行为，通过设计在线游戏，收集对话数据，对数据进行特征提取，建立分类器，探讨包括人口统计学信息、心理特征在内的背景因素对人们响应特定说服策略的影响。