Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023)

Abstract: 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.

## introduction attracting an increasing amount of attention, empathetic response generation aims to generate empa-next @xcite advmod is @xcite sentence 1: oh well , there is always next time. examples from the empathetic-dialogues dataset. sentence 1 shows the variability of emotional words. sentence 2 shows the correlations of emotional words with semantic roles. thetic responses by perceiving the speaker's emotional feelings @xcite @xcite @xcite @xcite . early methods perceive the speaker's feelings by understanding the holistically semantics and emotions expressed in the language of the context @xcite @xcite . these methods are prone to generate trivial and uninformed responses, ascribed to the neglect of nuances of human emotion in dialogues @xcite . to address this issue, recent methods detect emotional words in the language of the communicators and build them as static vectors to perceive subtle emotions @xcite @xcite @xcite . however, according to linguistic re-search @xcite @xcite , focusing on emotional words while ignoring their characteristics in the expression process leads to emotional misunderstandings and the neglect of words with important semantic information. as an important theory of emotional expression in linguistics, the conceptualization of emotions @xcite suggests that emotional words have two important characteristics in the expression process: variability and relevance. variability is that the affection of emotional words changes dynamically during the expression process. for example, "well" generally carries a positive meaning, but in sentence 1 (shown in figure 1 ), it is used as an interjection to express a neutral emotion. using static vectors (such as embedding @xcite or vad (mohammad, 2018)) to represent this dynamic emotion, previous methods are prone to misunderstand this sentence as positive. relevance refers to the grammatical correlations between emotional words and words carrying semantic meaning, which plays an important role in understanding emotions and semantics. for example, as shown in figure 1 , sentence 2 expresses the "exciting" emotion due to the victory of "team". "team" is the primary subject described in the sentence, carrying key semantic information. through the "[adj]-ccomp-[noun]" correlation, the emotional word "exciting" directly modifies "team". compared to previous work that did not consider such correlations, the model is more likely to identify key semantic words that are directly associated with emotional words through these types of syntax-meaningful relationships. therefore, focusing on the variability and relevance of emotional words can promote the correct recognition of emotions and the detection of important semantics. therefore, we propose a dynamical emotion-semantic correlation model (escm) for empathetic dialogue generation. escm dynamically constructs emotion-semantic vectors through the interaction of context and emotions. by encoding emotion-semantic vectors, the model dynamically adjusts emotions and semantics in the context to capture the variability of emotional words. to reflect the correlations between emotions and semantics clearly, we introduce a dependency tree. based on the dynamic emotion-semantic represen-tation and the dependency tree, escm proposes a dynamic correlation graph convolutional network to guide the model to capture the correlations between emotions and semantics clearly. by learning dynamic emotion-semantic representations and their correlations, escm accurately understands the emotions of the dialogue and captures important semantics to generate more empathetic responses. we conduct experiments on the empathetic-dialogues dataset @xcite . the results show that the escm model accurately understands the dialogue and generates grammatically fluent and informative empathetic responses. furthermore, we extract and statistically analyze the common correlation structures in dialogues from the empathetic-dialogue dataset. the results indicate that the correlations between emotion and semantics are frequently and extensively utilized in expressing emotions during conversations. additionally, the results of our analysis of correlation structures are consistent with linguistic conclusions @xcite . to sum up, our contributions are as follows: ## related work empathetic response generation refers to empathetically responding by perceiving emotional feelings in the language of the speaker @xcite . early approaches explore the overall emotions of the conversation. @xcite intro-duce emotion representation generated by a pretrained emotion classifier to learn and express specific types of emotions in the conversation. however, emotions expressed in responses are often diverse rather than specific @xcite . therefore, @xcite utilize multiple professional emotion listeners to express various appropriate emotions. @xcite group multiple conversation emotions by polarity and simulate the speaker's emotions to generate empathetic responses. these methods focus on the overall emotions of the conversation and ignore nuanced emotions @xcite . to capture nuanced emotions, @xcite extract emotional words through the nrc emotion lexicons @xcite and integrate them into the model. @xcite and @xcite introduce emotional cause detection models to capture emotional words and perceive nuanced emotions. @xcite enhance emotional representation in the context with additional knowledge, which helps detect emotional words. @xcite use commonsense reasoning knowledge to infer nuanced emotions in the conversation. @xcite employ pre-trained models to detect word-level emotion and keywords to detect the nuanced emotion in dialogues. these methods detect emotional words with nuanced emotions in the language of the conversation and use static vectors such as word embeddings or vad to represent emotional words. overall, early approaches ignore emotional words with nuanced emotions. recent methods ignore two major characteristics of emotional words in linguistic expression: variability and correlation. unlike these methods, we consider the two characteristics of emotional words and propose a dynamic emotion-semantic correlation model to better understand the conversation. ## conclusion and future work this paper proposes escm, which introduces two characteristics of emotions in the linguistic expression process: variability and the correlations between emotions and semantics. the proposed model constructs a dynamic emotion-semantic vector to reflect variability and uses a dependency treebased dynamic correlation graph convolutional network to learn correlations. both automatic and manual metrics demonstrate the effectiveness of the model. furthermore, we conduct statistical analysis experiments. the results show that correlations are frequently used in the dialogue. additionally, we find that the correlation structures in the dialogue are consistent with linguistic research. to further investigate the correlation between emotion and semantics, we will take into account pre-trained knowledge, multilinguality, personalization, and other factors in future work.