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:同情性对话是一种人性的行为,既需要感官因素(如情感状态),也需要认知性因素(如情感原因)和认知性因素(如情感原因),除了早期工作中的情感状态外,最近的方法研究情感原因,在同情性对话中研究情感原因。这些方法侧重于理解和复制情绪原因,以表达对演讲者的同情。然而,真实的共性反应不仅重复背景原因,而且往往显示从反应中的原因向反应中的原因的逻辑和情感偏向性转变。在这项工作中,我们提出了一个情感引发转变图,以明确模拟情感原因在同情性对话中两个相邻的转弯之间的自然转变。用这个图,下一个转弯的情感原因的概念词可以预测,并被一个专门设计的概念-觉解析器用于产生共性反应。基准数据集的自动和人类实验结果表明,我们的方法产生比现有模型更同情性、一致、信息化和具体的反应。