Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality
This work addresses the challenge of generating more empathetic responses in conversational AI by improving emotional understanding, though it appears incremental as it builds on existing methods by adding causality modeling.
The paper tackles the problem of empathetic response generation by modeling emotional causality, focusing on both the user's emotion and its cause, and demonstrates the effectiveness of their graph-based multi-hop reasoning model on the EMPATHETICDIALOGUES dataset compared to competitive models.
Empathetic response generation aims to comprehend the user emotion and then respond to it appropriately. Most existing works merely focus on what the emotion is and ignore how the emotion is evoked, thus weakening the capacity of the model to understand the emotional experience of the user for generating empathetic responses. To tackle this problem, we consider the emotional causality, namely, what feelings the user expresses (i.e., emotion) and why the user has such feelings (i.e., cause). Then, we propose a novel graph-based model with multi-hop reasoning to model the emotional causality of the empathetic conversation. Finally, we demonstrate the effectiveness of our model on EMPATHETICDIALOGUES in comparison with several competitive models.