Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023)

Abstract: In empathetic conversations, individuals express their empathy towards others. Previous work has mainly focused on generating empathetic responses by utilizing the speaker’s emotion. Besides, external commonsense knowledge has been applied to enhance the system’s understandings of the speaker’s situation. However, given an event, commonsense knowledge base contains various relations, potentially leading to confusion for the dialogue system. Consequently, inconsistencies arise among the emotion, generated response and speaker’s contextual information. To this end, we propose a novel approach for empathetic response generation, which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation. This selected knowledge is used to refine the commonsense cognition and empathy expression for generated responses. Experimental results show that our approach significantly outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses. Moreover, case studies highlight the interpretability of knowledge selection in the responses and the effectiveness of adaptive module in our model. Code: https://github.com/Hanscal/DCKS .

## introduction empathy is a desirable human ability in our daily conversations. it is known as a complex multidimensional construct encompassing social, cognitive, and emotional processes, which enables us to experience the emotion of others through various emotional stimuli and to understand the implicit mental states of others @xcite . previous research @xcite @xcite has been conducted on dialogue systems to enhance its empathy ability in open-domain. in order to generate empathetic responses, one line of growing interests is incorporating commonsense knowledge into conversation modeling @xcite @xcite . yet, understanding speaker's emotion and showing the contextually appropriate comprehension of her/his situation are still challenges in empathetic conversations. when interacting with a dialogue system, the speakers are not expected to explicitly share all the information about their situation and how they may feel. as humans, we use our commonsense knowledge to make connections between what is explicitly mentioned and what is implied. hence, to address above issues, some prior works @xcite implement external knowledge to identify the speaker's situation, to acknowledge the speaker's status and to bring diversity for generated response. however, straightforward knowledge merging method confuses the system and the response consistency would be deteriorated. this is demonstrated in figure 1 , where the irrelevant knowledge (need) may potentially form empathetic responses, which conflicts with the information about speaker's emotion (content). accordingly, the speaker displays the satisfaction of her/his expe-rience, which provides potential informative cognitions based on one unified commonsense. we can assume that if the most appropriate commonsense cognition (intent) is selected with respect to emotion status, the generated response shows better consistency and empathy. therefore, we believe dialogue systems with rectified knowledge, which aims at unifying the contextual emotion, lead to more consistent and empathetic responses. in this paper, we address the task of empathetic dialogue generation by dynamically infusing commonsense knowledge. such additional commonsense knowledge is used to improve the cognitive understanding about the speaker's situation and feelings, thus enhance the empathy expression in the generated responses. meanwhile, the dynamical selection stage avoids the confusion of knowledge in dialogue system and enhance the response consistency with context history. in general, our main contributions are summarized as follows: ## methodology our proposed model is built upon the transformerbased pre-trained language model to generate listener's utterance. each conversation process of the model is mainly divided into three stages: contextual probing, contextual unification workspace and knowledge-aware decoder. the overview of our model is illustrated in figure 2 . ## conclusions in this paper, we improve empathetic dialogue generation by infusing dynamical commonsense knowledge to promote the understanding of the speaker's situation and feelings, which leads to more consistent and empathetic responses. the automatic and human evaluation demonstrate that the effectiveness of our approach in high-quality empathetic response generation.