Integrating discourse features and response assessment for advancing empathetic dialogue

Abstract

Empathetic response generation is a crucial task in natural language processing, enabling emotionally resonant machine–human interactions. In this paper, we introduce the InfRa (Integrating Discourse Features and Response Assessment) model to address limitations in traditional methods for this task, such as the lack of deep dialogue comprehension and response control. InfRa integrates discourse features to augment structural dialogue understanding, with a novel edge pruning and mutual information learning module to further refine the representation. The model also employs a response evaluation module for dynamic optimization, ensuring emotional and semantic consistency between the generated response and its context. Our experiments demonstrate that InfRa outperforms existing baselines, reducing the Perplexity (PPL) score by approximately 9 points and excelling in all three fine-grained aspects of human evaluation. This research not only advances the development of empathetic chatbots but also provides valuable insights for broader text generation tasks.