基于深度学习的情感对话响应综述  

A Survey of Deep Learning Based Emotional Dialogue Response

作　　者：杨州 陈志豪 蔡铁城 王宇峰 廖祥文[1,2,3,4] YANG Zhou;CHEN Zhi-Hao;CAI Tie-Cheng;WANG Yu-Feng;LIAO Xiang-Wen(College of Computer and Data Science,Fuzhou University,Fuzhou 350108;Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou University,Fuzhou 350108;Digital Fujian Institute of Financial Big Data,Fuzhou 350108;Department Of New Networks,Peng Cheng Laboratory,Shenzhen,Guangdong 518000)

机构地区：[1]福州大学计算机与大数据学院,福州350108 [2]福州大学福建省网络计算与智能信息处理重点实验室,福州350108 [3]数字福建金融大数据研究所,福州350108 [4]鹏城实验室新型网络研究部,广东深圳518000

出　　处：《计算机学报》2023年第12期2489-2519,共31页Chinese Journal of Computers

基　　金：国家自然科学基金项目(No.61976054)资助。

摘　　要：人机对话作为人工智能的重要领域,以其方便快捷的交互特点广泛应用于任务型和闲聊型机器人等诸多商业场景,并被视为新一代人机交互的主要形式.但情绪感知与表达能力的缺乏致使人机对话技术在复杂交互场景中难以满足人们对情感交流的强烈需求.为弥补人机对话技术中情感智能的缺失,基于深度学习的情感对话响应任务被提出且已发展为对话领域中一个重要的研究方向.本文首先回顾了基于深度学习的情感对话响应任务的发展历程,其次按照任务将情感对话响应分为可控情感对话生成、共情对话响应、情绪支持、多模态情感对话生成、新任务五类.随后本文也按照常用的结构将模型进行了归类与分析,以求更细致地阐述各种结构在情感对话响应任务中的具体用法,之后介绍了常用数据与评测指标.最后本文也对模型进行了总结,并在此基础上进一步展望了该任务未来的发展方向.As an important field of artificial intelligence,human-machine dialogue is regarded as the main form of the new generation of human-machine interaction.Due to its convenience,human-machine dialogue is widely used in many business scenes,such as task-based dialogue systems and chat robots.In real-life scenes,conversations are often accompanied by emotional exchanges.However,without emotion perception and expression capabilities,human-machine dialogue technology fails to complete emotional communication in such scenes.In order to make up for the lack of emotional intelligence in human-machine dialogue technology,the deep learning based emotional dialogue response task has been proposed and developed into an important research direction in the field of dialogue.In this paper,we first review the development of deep learning based emotional dialogue response task.According to different goals,we then divide the task into five subtasks:controllable emotional dialogue generation,empathetic dialogue response,emotion support,multimodal emotion dialogue generation,and new tasks.Controllable emotional dialogue generation focuses on how to generate responses with specified emotions.According to different components for handling emotions,we divide these models into emotion perception models,emotion representation models,and emotion perception representation models.Empathetic dialogue response aims to automatically perceive emotions and express empathetic responses.According to different factors influencing empathetic perception and expression,we divide this category of models into emotion factor-based models,compound factor-based models,and structural factor-based models.Emotion support is to regulate the speaker’s emotion to comfort the user’s feelings.Due to the different types of emotions involved,we divide this type of models into three subtypes:stimulating positive emotions,stimulating specified emotions,and reducing negative emotions.Multimodal emotion dialogue generation models focus on multiple modalities,such as i

关 键 词：情感计算 情感分析 对话系统 文本生成 自然语言处理 深度学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]