Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality

TL;DR

This paper introduces a graph-based multi-hop reasoning model that captures emotional causality to improve empathetic response generation, addressing the gap of understanding why users feel certain emotions in conversations.

Contribution

The paper presents a novel graph-based model that explicitly models emotional causality with multi-hop reasoning, enhancing empathetic response generation.

Findings

Outperforms existing models on EMPATHETICDIALOGUES dataset

Effectively captures emotional causality in conversations

Improves the relevance and empathy of generated responses

Abstract

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.