The paper "Exploiting Emotion-Semantic Correlations for Empathetic Response Generation" addresses the challenge of generating empathetic responses by understanding the speaker's emotional feelings from dialogue. Traditional methods capture emotional words as static vectors, which can lead to misunderstandings of emotions and neglect of key semantics. To overcome these limitations, the authors propose a dynamic Emotion-Semantic Correlation Model (ESCM) that constructs dynamic emotion-semantic vectors through the interaction of context and emotions. ESCM uses dependency trees to reflect the correlations between emotions and semantics, and a dynamic correlation graph convolutional network to guide the model in learning context meanings and generating empathetic responses. Experimental results on the EMPATHETIC-DIALOGUES dataset show that ESCM accurately understands semantics and emotions and generates fluent and informative empathetic responses. The analysis also indicates that correlations between emotions and semantics are frequently used in dialogues, supporting the effectiveness of the proposed model. The contributions of the paper include introducing the expressive characteristics of emotions, proposing ESCM, and demonstrating its effectiveness through experiments and statistical analysis.The paper "Exploiting Emotion-Semantic Correlations for Empathetic Response Generation" addresses the challenge of generating empathetic responses by understanding the speaker's emotional feelings from dialogue. Traditional methods capture emotional words as static vectors, which can lead to misunderstandings of emotions and neglect of key semantics. To overcome these limitations, the authors propose a dynamic Emotion-Semantic Correlation Model (ESCM) that constructs dynamic emotion-semantic vectors through the interaction of context and emotions. ESCM uses dependency trees to reflect the correlations between emotions and semantics, and a dynamic correlation graph convolutional network to guide the model in learning context meanings and generating empathetic responses. Experimental results on the EMPATHETIC-DIALOGUES dataset show that ESCM accurately understands semantics and emotions and generates fluent and informative empathetic responses. The analysis also indicates that correlations between emotions and semantics are frequently used in dialogues, supporting the effectiveness of the proposed model. The contributions of the paper include introducing the expressive characteristics of emotions, proposing ESCM, and demonstrating its effectiveness through experiments and statistical analysis.