Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besides concerning emotion status in early work, the latest approaches study emotion causes in empathetic dialogue. These approaches focus on understanding and duplicating emotion causes in the context to show empathy for the speaker. However, instead of only repeating the contextual causes, the real empathic response often demonstrate a logical and emotion-centered transition from the causes in the context to those in the responses. In this work, we propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue. With this graph, the concept words of the emotion causes in the next turn can be predicted and used by a specifically designed concept-aware decoder to generate the empathic response. Automatic and human experimental results on the benchmark dataset demonstrate that our method produces more empathetic, coherent, informative, and specific responses than existing models.

翻译：共情对话是一种人类化行为，需要同时感知情感因素（如情绪状态）和认知因素（如情绪产生的原因）。除了早期工作中关注的情绪状态外，最新方法开始研究共情对话中的情感原因。这些方法侧重于理解并复述对话情境中的情感原因，以向说话者表达共情。然而，真实的共情回复并非仅重复情境中的原因，而是通常展现出从情境原因到回复原因之间逻辑清晰且以情感为中心的转换。本文提出一种情感原因转换图，用于显式建模共情对话中相邻两轮之间情感原因的自然过渡。借助该图，可预测下一轮情感原因的概念词，并由专门设计的基于概念的解码器用于生成共情回复。在基准数据集上的自动评估和人工实验结果表明，与现有模型相比，本方法生成的回复更具共情性、连贯性、信息性和特异性。