Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation
This addresses the need for more nuanced empathetic dialogue systems, though it is incremental by building on prior work that considered single factors.
The paper tackled the problem of generating empathetic responses in conversations by integrating emotion cause extraction, knowledge graphs, and communication mechanisms, resulting in more informative and empathetic responses as demonstrated on a benchmark dataset.
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.