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.

情感回应生成是理解对话表述中的认知和情感状态并生成适当回应。本文提出了一种迭代联想记忆模型 (IAMM)，通过使用新颖的二阶交互注意机制，迭代捕捉对话表述和情境、对话历史以及记忆模块 (用于存储关联词) 之间的重要关联词，从而准确而细致地理解表述。实验验证了该模型的有效性，并且语言模型的变化实验也表明关注关联词可以提高情感的理解和表达。

情感回应生成的迭代联想记忆模型