Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturing and understanding associated words across dialogue utterances. However, existing approaches regard dialogue utterances as either a long sequence or independent utterances for comprehension, which are prone to overlook the associated words between them. To address this issue, we propose an Iterative Associative Memory Model (IAMM) for empathetic response generation. Specifically, we employ a novel second-order interaction attention mechanism to iteratively capture vital associated words between dialogue utterances and situations, dialogue history, and a memory module (for storing associated words), thereby accurately and nuancedly comprehending the utterances. We conduct experiments on the Empathetic-Dialogue dataset. Both automatic and human evaluations validate the efficacy of the model. Variant experiments on LLMs also demonstrate that attending to associated words improves empathetic comprehension and expression.

翻译：共情回复生成旨在理解对话语句中的认知与情感状态，并生成恰当的回复。心理学理论指出，理解情感与认知状态需要迭代地捕捉和理解对话语句间的关联词。然而，现有方法将对话语句视为长序列或独立语句进行理解，容易忽略其间的关联词。为解决这一问题，我们提出一种用于共情回复生成的迭代关联记忆模型。具体而言，我们采用一种新颖的二阶交互注意力机制，迭代地捕捉对话语句与情境、对话历史以及记忆模块（用于存储关联词）之间的关键关联词，从而准确而细致地理解语句。我们在Empathetic-Dialogue数据集上进行了实验。自动评估与人工评估均验证了模型的有效性。在大型语言模型上的变体实验也表明，关注关联词能提升共情理解与表达能力。