Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation (2022)

Abstract: Expressing empathy is important in everyday conversations, and exploring how empathy arises is crucial in automatic response generation. Most previous approaches consider only a single factor that affects empathy. However, in practice, empathy generation and expression is a very complex and dynamic psychological process. A listener needs to find out events which cause a speaker’s emotions (emotion cause extraction), project the events into some experience (knowledge extension), and express empathy in the most appropriate way (communication mechanism).To this end, we propose a novel approach, which integrates the three components - emotion cause, knowledge graph, and communication mechanism for empathetic response generation.Experimental results on the benchmark dataset demonstrate the effectiveness of our method and show that incorporating the key components generates more informative and empathetic responses.

## introduction according to @xcite , empathy is an affective response more appropriate to another's situation than one's own, which is the spark of human concern for others and the glue that makes social life possible. it is a complex human trait and dynamic psychological process related to emotion and cognition, where emotional empathy refers to vicarious sharing of emotion and cognitive empathy refers to mental perspective taking @xcite . since 1990s, the study of empathy has been widely applied to mental health support @xcite , quality of care improvement @xcite , and intelligent virtual assistants @xcite . expressing empathy becomes more important in today's dialogue systems. however, there are challenges in developing an empathetic model, such as preparing a proper training corpus, learning to get a comprehensive understanding of the dialogue context, and designing an appropriate empathy expression strategy. recently, there has been some work to address these issues. a standard benchmark containing large-scale empathetic conversations was proposed, laying the cornerstone of empathetic dialogue research @xcite . some researchers try to gain a deeper understanding of contextual information. for example, @xcite applied an emotion cause extractor to conversations and used the extracted causes to guide the response generation process. @xcite incorporated external commonsense information to enrich the context. during the language generation process, some researchers focus on controlling emotions of generated responses using emotional blending to imitate the speakers' emotions @xcite . all the above work considers only a single aspect that affects empathy. however, in practice, empathy generation and expression is a very complex and dynamic process. according to research work in the field of psychological science, we believe that three different but related factors matter in empathy: emotion (the automatic proclivity to share emotions with others), cognition (the intersubjectivity to interpret others' intentions and feelings while keeping separate self and other perspectives), and behavioral outcome (the actions to express empathy) @xcite . consequently, we divide the entire empathy process into five functional modules: emotion perception, cause extraction, experience projection, dialogue reaction, and verbal expression. specifically, emotion perception aims to sense emotions from others. cause extraction is to determine detailed events corresponding to the emotions. experience projection enriches the contextual information through knowledge extension from the emotion causes. dialogue reaction decides the response strategies by learning from the contexts. verbal expression is the final step in a dialogue system to generate responses in terms of languages. towards this end, we propose a novel approach imagine, a.k.a. integrating emotion causes, knowledge, and communication mechanisms for empathetic dialogue generation. using these components improves cognitive understanding of contexts and enhances empathy expression in the generated responses. our framework involves three stages -emotion cause extraction, knowledgeenriched communication, and response generation. we evaluate our approach on the empatheticdi-alogues dataset. extensive experimental results demonstrate the effectiveness of imagine in automatic and human evaluations, showing that our approach generates more informative and empathetic responses (an example is shown in figure 1 ). our contributions can be summarized as follows: ## approach our proposed model, imagine, is built upon the standard transformer @xcite and its overview is illustrated in figure 2 . it has three stages consisting of five functional modules: emotion cause extraction (emotion perception, cause extraction), knowledge-enriched communication (dialogue reaction, experience projection), and response generation (verbal expression). emotion perception predicts emotions of the input. cause extraction extracts causes related to the emotions from the input. experience projection acquires knowledge based on the causes mentioned above. dialogue reaction decides the response strategies by learning from the contexts. verbal expression integrates the information obtained from the above four modules and generates appropriate responses. ## conclusion this paper presents a novel framework that integrates emotion causes, knowledge graphs, and communication mechanisms for empathetic response generation. the emotion cause detection allows us to determine what events stimulate a user's emotion. we can understand the events with the knowledge graph, enriching the contextual information. furthermore, the communication mechanisms enhance our ability to let users feel that we are tryining to feel what they feel. automatic and human evaluations show that our proposed approach can generate more informative and empathetic responses.