Emotional dialog generation via multiple classifiers based on a generative adversarial network

作     者：Wei CHEN Xinmiao CHEN Xiao SUN 

作者机构：School of Computer and InformationHefei University of TechnologyHefei 230601China 

出 版 物：《Virtual Reality & Intelligent Hardware》 (虚拟现实与智能硬件（中英文）)

年 卷 期：2021年第3卷第1期

页      面：18-32页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081203[工学-计算机应用技术] 08[工学] 081104[工学-模式识别与智能系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Emotional dialog generation Sequence-to-sequence model Emotion classification Generative adversarial networks Multiple classifiers 

摘      要：Background Human-machine dialog generation is an essential topic of research in the field of natural language *** high-quality,diverse,fluent,and emotional conversation is a challenging *** on continuing advancements in artificial intelligence and deep learning,new methods have come to the forefront in recent *** particular,the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate ***,neural network models come with their own set of questions and *** basic conversational model framework tends to produce universal,meaningless,and relativelysafe*** Based on generative adversarial networks(GANs),a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog *** proposed model comprises a generative and three discriminative *** generator is based on the basic sequence-to-sequence(Seq2Seq)dialog generation model,and the aggregate discriminative model for the overall framework consists of a basic discriminative model,an emotion discriminative model,and a fluency discriminative *** basic discriminative model distinguishes generated fake sentences from real sentences in the training *** emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion,and directs the generative model to generate dialogs that correspond to the category of the pre-specified ***,the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent *** Based on the experimental results,this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy,fluency,and *** The proposed EMC-