  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.
