An Iterative Associative Memory Model for Empathetic Response Generation (2024)

Abstract: 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.

## introduction as an important task for improving dialogue quality, empathetic response generation aims to comprehend the emotional and cognitive states of the user in dialogue utterances and provide appropriate responses @xcite @xcite @xcite . the majority of methods treat the dialogue utterances as a long sequence to comprehend the user states @xcite @xcite @xcite . these approaches ignore the discrepancies in meanings among individual utterances, leading to inaccurate understanding of emotional and cognitive states @xcite . to address this issue, some methods comprehend more delicate emotional and cognitive states within a set of independent utterances by distinguishing selfother awareness @xcite or emphasizing emotion-intent transitions @xcite . however, the situation model @xcite , as an important theory for understanding empathy, posits that comprehending emotional and cognitive states in detail necessitates not only understanding independent utterances, but also iteratively associating pivotal associated words within those utterances @xcite @xcite . as shown in figure 1 , the speaker's independent utterances imply the emotion of "anger," yet the overall expression conveys "furious." if the utterances are understood independently, the listener is likely to misinterpret the speaker's emotion as "anger." in contrast, the iterative association integrates subtle associated words, allowing for an accurate understanding of the dialogue. specifically, when faced with the first utterance, the listener combines this utterance with related words from the situation and stores it in its memory to form an initial understanding. when encountering the speaker's second utterance, the listener meticulously compares and reasons this utterance with the dialogue history, situation, and related words in memory, to deepen its understanding of the utterance. for instance, associating "jerks" and "guy" reveals an intensification of the emotion of anger, i.e., "furious". additionally, reasoning that "cut me off" and "caused an accident" makes it easier to realize the speaker's furious due to the life-threatening event. overall, by associating explicit and implicit information, the listener attains a more nuanced understanding of the utterances. while this comprehension process proves effective, simulating it to achieve meticulous understanding of dialogues remains an open challenge. in this paper, we propose an iterative associative memory model (iamm) that iteratively employs an information association module to identify and learn subtle connections within both explicit and implicit information. we first treat the dialogue content, including both the dialogue utterances and situations, as explicit information, and treat the reasoning knowledge about the dialogue content generated by comet @xcite as implicit information. subsequently, we iteratively utilize the information association module to identify and learn associated words between utterances and situations, dialogue history, and memory (initialized as an empty set) in the explicit/implicit information, and store them in the memory for a thorough understanding of the utterances. specifically, the information association module, inspired by the idea that "pages (nodes) linked by important pages (nodes) are also more important" @xcite , effectively identifies associated words in the to-be-associated sentences through a second-order interaction attention mechanism. to validate our model, we construct iamm and iamm large (llms-based model). experiments are conducted on the empathetic-dialogue dataset @xcite . both automatic evaluation and human evaluation demonstrate that compared with the state-of-the-art baselines, our models possess stronger understanding while expressing more informative responses. overall, our contributions are as follows: ## related work empathetic response generation requires comprehending the user states in dialogue utterances to generate appropriate responses @xcite . existing methods can be categorized into dialogue-level models and utterance-level models, according to whether they understand dialogue utterances independently. dialogue-level models. dialogue-level models view all dialogue utterances holistically as a long sequence to comprehend user states. some dialogue-level models focus on the coarse emotions @xcite @xcite or subtle emotions @xcite @xcite @xcite present in a conversation to understand the user states. while focusing on emotional states, these models ignore cognitive states, leading to inadequate empathy comprehension @xcite . some methods introduce reasoning knowledge to more comprehensively attend to user states @xcite @xcite . utterance-level models. utterance-level models focus on differences in emotional and cognitive states within individual utterances. these models view dialogue utterances as independent sentences to understand the user's state within them via selfother awareness differentiating @xcite and emotion-intent transitions @xcite . the aforementioned methods view the dialogue utterances as either long sequences or as independent sentences to understand the user's emotions and cognitive states. however, understanding emotional and cognitive states requires iteratively capturing and comprehending associated words @xcite @xcite . therefore, we propose an iterative associative memory model (iamm), which iteratively understands the associated words between utterances from both explicit and implicit information, and generates empathetic responses. ## conclusion and future work in this paper, we have proposed an iterative associative memory model (iamm) for empathetic response generation, inspired by the human iterative process of understanding emotions and cognition. it employs a novel second-order interaction attention mechanism to iteratively identify key associated words across dialogue utterances, enabling a more accurate understanding of the emotional and cognitive states. automatic and human evaluations demonstrate that iamm accurately understands emotions and expresses more empathetic responses. experiments based on large language models and associated word analysis further validate the effectiveness of the iterative associations. in the future, we will explore empathetic comprehension mechanisms based on large language models.