Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems (2022)

Abstract: Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation. Existing work for empathetic dialogue generation concentrates on the two-party conversation scenario. Multi-party dialogues, however, are pervasive in reality. Furthermore, emotion and sensibility are typically confused; a refined empathy analysis is needed for comprehending fragile and nuanced human feelings. We address these issues by proposing a novel task called Multi-Party Empathetic Dialogue Generation in this study. Additionally, a Static-Dynamic model for Multi-Party Empathetic Dialogue Generation, SDMPED, is introduced as a baseline by exploring the static sensibility and dynamic emotion for the multi-party empathetic dialogue learning, the aspects that help SDMPED achieve the state-of-the-art performance.

## introduction empathetic conversation studies have been coming to the forefront in recent years owing to the increasing interest in dialogue systems. empathetic dialogues not only provide dialogue partners with highly relevant contents but also project their feelings and convey a special emotion, that is, empathy. as revealed by previous studies @xcite , empathy can enhance conversation quality and transmit appropriate emotional responses to partners. accordingly, most, if not all, existing work focuses on taking an emotional perspective in dialogue studies @xcite @xcite @xcite . although the empathetic conversation has received extensive attention, its exploration is still limited to the scenario with only two parties. in fact, multi-party chatting scenes are common in seminar discussions, conferences, and group chats. __ i have been fighting for so long. and i don't want to talk this with my family or friends. i'm confused and lonely. oh, i am so sorry. it's too bad for you. weak sensibility moderate sensibility strong sensibility ____ you are not alone. just be relaxed, i went through this and i truly understand how bad it is. gee! believe you can achieve it! @xcite high low sensibility: emotion: negative positive happy to hear that. working together, we can make the future better! another challenge is the way to model the fragile and nuanced feelings of dialogue participants. we first clarify the relations of sensibility, emotion, and empathy in this study. previous empathy studies recognized the emotion of one party and generated dialogues coupled with the same emotion @xcite . however, empathy is also determined by sensibility, which is a perspective-taking ability to experience other partners' emotions and make an appropriate response with his/her own view. according to the response "i went through this" in figure 1 , we can find that speaker 4 has a similar experience to speaker 1, while speaker 2 can only provide superficial comfort to speaker 1 due to his/her weak sensibility. we observe that sensibility arises from personality and experience, and remains static throughout a conversation. on the other hand, emotion may dynamically change. for example, speakers 2, 3, and 4 possess different sensibilities to speaker 1, and these personal background-related attributes are persistent in the conversation. by contrast, the emotion of speaker 1 gets reversed after receiving positive replies, as well as the main tone of this dialogue. to comprehensively cope with the aforementioned challenges in this study, we present a static-dynamic model for multi-party empathetic dialogue generation called sdmped. sdmped models multi-party dialogues by constructing a dynamic graph network with temporal information and explores participants' dynamic emotions and static sensibilities by fusing speaker information. the contributions of our work are as follows: ## model in this section, we introduce a static-dynamic model called sdmped as shown in figure 2 . we begin by describing the construction of the temporal dynamic graph network (tdgcn), including speaker sensibility nodes, emotion-related utterance nodes, and various types of edges between them. thereafter, we use tdgcn to obtain dynamic emotions and static speaker sensibilities by integrating nodes and edges. finally, we use prompt tuning to generate final dialogue responses based on emotion and sensibility information. ## conclusions and future work we have introduced a novel task called multi-party empathetic dialogue generation.we have proposed a model called sdmped suitable for the characteristics of the task. our experiments have demonstrated that sdmped is superior to other approaches on mped. future work can explore related issues such as integrating empathy into the dialogues, combining emojis and responses, guiding the active development of conversation.