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.

翻译：近期共情响应生成方法通过融入情感因果关系以增强对用户感受和经历的理解。然而，这些方法存在两个关键问题：一是仅考虑用户情感与经历之间的因果关系，忽略了用户经历之间的因果关联；二是忽视因果关系间的相互依赖性，对它们进行独立推理。为解决上述问题，我们提出在给定用户情感、对话历史及未来对话内容的前提下，同步交互式地推理所有可能的因果关系，进而将这些因果信息注入响应生成过程以产生共情响应。具体而言，我们设计了条件变分图自动编码器（CVGAE）用于因果推理，并在解码器中采用多源注意力机制实现因果信息注入。我们将整体框架命名为CARE（共情对话因果推理的缩写）。实验结果表明，本方法达到了当前最优性能。