CARE : Causality Reasoning for Empathetic Responses by Conditional Graph Generation (2022)

Abstract: Recent approaches to empathetic response generation incorporate emotion causalities to enhance comprehension of both the user’s feelings and experiences. However, these approaches suffer from two critical issues. First, they only consider causalities between the user’s emotion and the user’s experiences, and ignore those between the user’s experiences. Second, they neglect interdependence among causalities and reason them independently. To solve the above problems, we expect to reason all plausible causalities interdependently and simultaneously, given the user’s emotion, dialogue history, and future dialogue content. Then, we infuse these causalities into response generation for empathetic responses. Specifically, we design a new model, i.e., the Conditional Variational Graph Auto-Encoder (CVGAE), for the causality reasoning, and adopt a multi-source attention mechanism in the decoder for the causality infusion. We name the whole framework as CARE, abbreviated for CAusality Reasoning for Empathetic conversation. Experimental results indicate that our method achieves state-of-the-art performance.

## introduction empathy is the capability to perceive, understand and respond to another individual's feelings, experiences and situation @xcite . it is composed of two aspects @xcite , which are (i) affection, i.e., emotion understanding and appropriate emotional reaction @xcite , and (ii) cognition, i.e., comprehension and reasoning of the other's experiences and situation @xcite . earlier work on empathetic response generation merely pays attention to affection @xcite @xcite . consequently, their models lack understanding of the user emotion: impressive user: my brother got a custom-made bowling-ball, and ever since then he's been killing the game! it is awesome to see. our response: that is so cool. i bet he is a great player. figure 1 : causality reasoning results of gee mime @xcite and our proposed method in a real case. arrows indicate relations from cause to effect, while strikeout arrows indicate no causal relations. gee mime detects only direct causes and effects of the user's emotion independently, while ours extends the causality scope and reasons causalities interdependently. to solve these problems, we propose to reason all plausible causalities, i.e., causalities stated explicitly in the dialogue history and probably in the future dialogue, interdependently and simultaneously by formulating the reasoning as a conditional graph generation task. specifically, we aim to generate a causal graph foot_0 containing all plausible causalities conditioned on the user's emotion, dialogue history, and predicted future dialogue content. inspired by the variational graph auto-encoder (vgae) (kipf and welling, 2016), we design a conditional variational graph auto-encoder (cv-gae), which uses latent variables for conditional structure prediction, to accomplish causality reasoning. accordingly, the model is expected to have a deeper understanding of the user's feelings and experiences. in addition, some feelings and experiences, which are not explicitly stated in dialogue history but contribute to response generation, can be inferred in this process as shown in figure 1 . in this paper, we propose a novel empathetic response generation model, called care (causality reasoning for empathetic conversation). care reasons all plausible causalities by cvgae, and infuses them into response generation by a multisource attention mechanism in the decoder. in addition, we adopt multi-task learning to integrate causality reasoning and response generation during training. the experimental results on the empa-theticdialogues @xcite benchmark suggest that our method improves the model's understanding of user's feelings and experiences, and care achieves state-of-the-art performance on empathetic response generation. our main contributions are three-fold: ## related work since empathy is a critical character for social chatting systems @xcite , many studies have contributed to empathetic response generation. earlier work mainly focuses on the affective aspect of empathy. moel @xcite adopts a mixture of experts architecture to combine outputs from different decoders, each of which represents one emotion. based on the idea of emotion mixture, mime @xcite takes emotion polarity (positive or negative) into account. moreover, it uses emotion stochastic sampling and emotion mimicry to generate empathetic responses. @xcite propose to capture nuances of emotion at the token-level for decoding. moreover, an adversarial learning framework is leveraged to involve user feedback. having realized that ignorance of cognition impedes empathy in conversation, some recent methods involve both affection and cognition by incorporating emotion causes and effects. @xcite incorporate emotion causes into empathetic response generation by multi-hop reasoning from emotion causes to emotion states. @xcite identify emotion causes from dialogue context, and use gates at the decoder to control the involvement of these emotion causes in the response generation. @xcite emphasize emotion causes in dialogue context by a rational speech act framework. these three methods identify emotion causes via a classifier, which detects whether there is a causal relationship between a conversation fragment and an emotion statement or word each time. cem @xcite uses comet, an if-then commonsense generator, to generate causes and effects of user experiences, and refines dialogue context with them for response generation. however, all these methods obtain causalities independently. ## conclusion in this paper, we propose to reason all plausible causalities in conversation interdependently and simultaneously for a deep understanding of the user's feelings and experiences in empathetic dialogue. further, we turn the causality reasoning problem into a conditional graph generation task. correspondingly, we design cvgae, which uses latent variables for conditional structure prediction, and predicted future conversation content, to implement the reasoning. the reasoned causalities are infused into response generation for the final empathetic responses by a multi-source attention mechanism in the decoder. this whole structure is named as care (causality reasoning for empathetic conversation). experimental results show that care outperforms prior methods in terms of both automatic and manual evaluations.