  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.
