Empathetic response generation aims to generate empathetic responses by
understanding the speaker's emotional feelings from the language of dialogue.
Recent methods capture emotional words in the language of communicators and
construct them as static vectors to perceive nuanced emotions. However,
linguistic research has shown that emotional words in language are dynamic and
have correlations with other grammar semantic roles, i.e., words with semantic
meanings, in grammar. Previous methods overlook these two characteristics,
which easily lead to misunderstandings of emotions and neglect of key
semantics. To address this issue, we propose a dynamical Emotion-Semantic
Correlation Model (ESCM) for empathetic dialogue generation tasks. ESCM
constructs dynamic emotion-semantic vectors through the interaction of context
and emotions. We introduce dependency trees to reflect the correlations between
emotions and semantics. Based on dynamic emotion-semantic vectors and
dependency trees, we propose a dynamic correlation graph convolutional network
to guide the model in learning context meanings in dialogue and generating
empathetic responses. Experimental results on the EMPATHETIC-DIALOGUES dataset
show that ESCM understands semantics and emotions more accurately and expresses
fluent and informative empathetic responses. Our analysis results also indicate
that the correlations between emotions and semantics are frequently used in
dialogues, which is of great significance for empathetic perception and
expression.

我们提出了一个动态的情感 - 语义相关模型（ESCM）来帮助生成共情性对话，它通过上下文和情感的交互构建了动态的情感 - 语义向量并引入了依赖树来反映情感和语义之间的相关性，通过动态的相关图卷积网络引导模型学习对话中的上下文含义并生成具有流畅且丰富信息的共情性回应。实验结果表明，ESCM 能更准确地理解语义和情感，并表达出具有流畅且信息丰富的共情性回应，分析结果也显示出对话中情感和语义之间的相关性的频繁使用对共情感知和表达具有重要意义。