Emp-EEK: generating empathetic responses via exemplars and external knowledge

Abstract

Empathy plays a crucial role in human communication, and empathetic dialogue systems have garnered increasing research interest. However, accurately modeling and quantifying empathy remains challenging due to its inherently complex and multifaceted nature. Exemplar-based guidance has shown promise in enhancing empathetic response generation, yet existing approaches suffer from limitations such as noisy or irrelevant exemplars. To address these challenges, we propose Emp-EEK, an Empathetic response generation model guided by Exemplars and External Knowledge. Specifically, we employ a fine-tuned Dense Passage Retriever to jointly retrieve relevant exemplars based on both utterance-exemplar similarity and contextual proximity, ensuring more precise guidance for response generation. Furthermore, to enhance the system’s understanding of the speaker, we integrate external knowledge into the dialogue history, enriching contextual comprehension. To further elevate the level of empathy in responses, we introduce a multi-expert system that incorporates three independent decoders at the decoding stage. This design enables the model to effectively learn and capture the three key psychological mechanisms of empathetic communication: emotional reaction, interpretation, and exploration. Experimental results on the Empathetic-Dialogues dataset, evaluated through both automatic metrics and human judgments, demonstrate the effectiveness of our approach. Additionally, case studies analyzing the decoding process of different decoders highlight the strong interpretability of our model. Our code is publicly available at https://github.com/NEUWzk/Emp-EEK.