Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation

TL;DR

This paper introduces a novel neural approach for empathetic response generation that integrates emotion cause extraction, knowledge graphs, and communication mechanisms to produce more empathetic and informative responses in conversations.

Contribution

It presents an innovative method combining three key components—emotion cause, knowledge graph, and communication mechanism—for improved empathetic response generation.

Findings

Enhanced response informativeness and empathy

Effective integration of emotion cause and knowledge graph

Outperforms previous methods on benchmark datasets

Abstract

Expressing empathy is important in everyday conversations, and exploring how empathy arises is crucial in automatic response generation. Most previous approaches consider only a single factor that affects empathy. However, in practice, empathy generation and expression is a very complex and dynamic psychological process. A listener needs to find out events which cause a speaker's emotions (emotion cause extraction), project the events into some experience (knowledge extension), and express empathy in the most appropriate way (communication mechanism). To this end, we propose a novel approach, which integrates the three components - emotion cause, knowledge graph, and communication mechanism for empathetic response generation. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and show that incorporating the key components generates more informative and empathetic responses.