Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation (2023)

Abstract: Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user’s experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user’s perspective, ignoring the system’s perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user’s perspective (user’s desires and reactions) and the system’s perspective (system’s intentions and reactions). We enhance ChatGPT’s ability to reason for the system’s perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations.

## introduction empathy is a desirable capacity of humans to place themselves in another's position to show understanding of his/her experience and feelings and respond appropriately. empathy involves both cognitive and affective aspects @xcite , including the ability to perceive the user's situation and express appropriate emotions. previous work on empathetic response generation has primarily focused on the affective aspect of emotional expression @xcite @xcite ) by emotion detection, without sufficient consideration of context understanding. recently, there has been a growing interest in exploring context understanding by leveraging external commonsense knowledge for reasoning emotion causes-effects or the user's desires, such as @xcite and @xcite . figure 1 : two examples to produce a response from different perspectives. the blue solid box contains "xreact" and "xwant" representing the user's emotional reaction and desires. the green dotted box comprises "xreact" and "xintent," representing the emotional reaction and intention of the actual responder. however, these approaches focus on understanding the causalities from the user's perspective. exploring the causality within the user's context and reasoning his/her desires can be helpful so that the system's intention is aligned with the user's desires, and the response is generated from the user's perspective (figure 1(a) ). however, in real human communication, the responder's intention is not always confined to the user's desires, as shown in figure 1 @xcite . relying solely on the user's desire to generate a response may not fully understand the user's experience, and leads to weak empathy, as shown in figure 1 @xcite . therefore, it is necessary to incorporate both the user's perspective (exploring his/her desire and reaction) and the system's perspective (reasoning its intention and reaction to mimic humans) for empathetic response generation. through the utilization of comet @xcite , which is a pre-trained gpt-2 model @xcite ) fine-tuned on the if-then reasoning graph from atomic @xcite , the system's possible intentions can be predicted to align with the user's desires. however, the system's intention may not be constrained by the user's desire. therefore, we do not adopt comet for the system's intention reasoning. chatgpt 1 has shown its efficacy in several tasks @xcite . @xcite introduced chatgpt's potential in causal reasoning on humanannotated explainable causal reasoning dataset (e-care) @xcite . however, it is based on whether the model can make a judgment on correct causes or effects instead of generating causality explanations. in this paper, we propose to enhance it by incorporating in-context learning with commonsense reasoning for causality explanation. our main contributions are as follows: ## proposed method figure 2 shows an overview of our proposed method. it consists of three components: (1) causality reasoning module, which aims to enhance the chatgpt or t5 decoder with a causality explanation for empathetic response generation. (2) enhanced chatgpt-based response generation. (3) t5-based response generation, which is based on a trained t5 encoder-decoder to be compared with other approaches that have developed their own model using the empatheticdialogue dataset @xcite @xcite @xcite . ## evaluations on chatgpt-based response generation then, we evaluate the responses generated by chat-gpt. ## experiments on t5-based response generation finally, we evaluate the responses generated by the t5-based model. ## conclusions and future work we have proposed a commonsense-based causality explanation approach for diverse empathetic response generation that considers the system's intentions and reactions as well as the user's desires and reactions. specifically, we enhance chatgpt's ability to reason the system's intentions and reactions by integrating in-context learning with commonsense knowledge (desire, reaction, and intention). we have integrated the commonsense-based causality explanation with both chatgpt and a trained t5 model. the experimental results demonstrate that our method outperforms other competitive methods on both automatic and human evaluations. in the future, we will explore fine-grained approaches for causality explanation from the perspective of both the user and the system. introduction: assuming that you are sys, who is a friend of the user. you are empathetic sometimes. in this task, you are given the user's input and the information of "user wants to:" and "user reacts to:": "user wants to:", which means what the user wants to do after the input; "user reacts to:", which means how the user react to the input. after that, please reason about the following two parts: "sys's intent:": which means what the sys wants to do after the input, or what's the intent of sys to respond to the input; "sys reacts to:", which means how the sys reacts to the input. then you respond (should be concise, no more than 30 words) to the input based on the information of user's input, "user wants to:", "user reacts to:", "sys's intent:", "sys reacts to:". "sys:": which means the response of sys. please generate the following three parts in the format below: sys's intent: sys reacts to: sys: user2: my family is coming to visit! sys2: awesome. when are they coming and for how long? user2: they are coming next year from africa! example @xmath0: to have a good time. to go to the airport. to have fun with the family. <@xmath1: happy. excited. happy. excited. loved. <@xmath2: to see the sights. to be with family. to be with them. to have fun. <@xmath3: happy. excited. satisfied. tired. relieved. response2 sys2: that's a long trip. i hope they have a good time.