Empathetic response generation is 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. Meanwhile, 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和情感状态，并生成恰当的回复。心理学理论认为，理解情感和认知状态需要迭代地捕捉和理解对话语句中的关联词汇。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，容易忽视语句之间的关联词汇。为解决这一问题，我们提出了一种用于共情回复生成的迭代联想记忆模型（IAMM）。具体而言，我们采用了一种新颖的二阶交互注意力机制，以迭代方式捕捉对话语句与情境、对话历史以及存储关联词汇的记忆模块之间的关键关联词汇，从而准确且细腻地理解语句。我们在Empathetic-Dialogue数据集上进行了实验。自动评估和人工评估均验证了该模型的有效性。同时，针对大语言模型（LLMs）的变体实验也表明，关注关联词汇能够提升共情理解与表达能力。