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.

翻译：共情回复生成旨在通过理解对话语言中说话者的情感感受，生成共情回应。近期方法捕捉对话者语言中的情感词汇，并将其构建为静态向量以感知细微情感。然而，语言学研究表明语言中的情感词汇具有动态性，且与语法中其他语义角色（即具有语义含义的词汇）存在关联。先前方法忽略了这两个特征，容易导致情感误解和关键语义忽视。为解决此问题，我们提出动态情感-语义关联模型（ESCM）以用于共情对话生成任务。ESCM通过上下文与情感的交互构建动态情感-语义向量。我们引入依存树来反映情感与语义之间的关联。基于动态情感-语义向量和依存树，我们提出动态关联图卷积网络，引导模型学习对话中的上下文含义并生成共情回复。在EMPATHETIC-DIALOGUES数据集上的实验结果表明，ESCM能更准确地理解语义和情感，并生成流畅且信息丰富的共情回复。分析结果还表明，情感与语义之间的关联在对话中频繁使用，这对于共情感知和表达具有重要意义。