Empathetic Response Generation System: Enhancing Photo Reminiscence Chatbot with Emotional Context Analysis
Alberto Herrera, Xiaobei Qian, Li‐Chen Fu · 2024
Dementia affects 50 million people worldwide, underscoring the urgent need for effective interventions to enhance their well-being. While reminiscence intervention shows promise, its implementation is hindered by limited human resources, making machine-aided systems a viable automated solution for seamless photo-reminiscence sessions. In this paper, we introduce an empathetic response generation system specifically designed to enhance a question-only photo-reminiscence chatbot, with a focus on improving emotional context understanding and enhancing conversation engagement. We leverage Transformers to encode dialogue history, infer emotional states from user responses, and extract named entities. By combining template-based utterances with a retrieval chatbot, our system generates relevant and empathetic responses to user replies. Our system’s effectiveness is validated through human evaluations using a Likert-like scale to assess engagement levels. The results demonstrate that our approach surpasses both the question-only system and other models from existing works, including retrieval and generated models. This highlights our system’s potential to enhance interactions and engagement, advancing technology-driven interventions for dementia that improve well-being and quality of life.