CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement Learning
This addresses the need for more emotionally supportive chatbots in human-computer interaction, though it appears incremental by building on existing chatbot methods with empathy enhancements.
The study tackled the problem of creating empathetic chatbots by developing CheerBots, which use reinforcement learning to understand users' implied feelings and reply empathetically over multiple dialogue turns, resulting in outperforming baseline chatbots in automatic metrics and human ratings.
Apart from the coherence and fluency of responses, an empathetic chatbot emphasizes more on people's feelings. By considering altruistic behaviors between human interaction, empathetic chatbots enable people to get a better interactive and supportive experience. This study presents a framework whereby several empathetic chatbots are based on understanding users' implied feelings and replying empathetically for multiple dialogue turns. We call these chatbots CheerBots. CheerBots can be retrieval-based or generative-based and were finetuned by deep reinforcement learning. To respond in an empathetic way, we develop a simulating agent, a Conceptual Human Model, as aids for CheerBots in training with considerations on changes in user's emotional states in the future to arouse sympathy. Finally, automatic metrics and human rating results demonstrate that CheerBots outperform other baseline chatbots and achieves reciprocal altruism. The code and the pre-trained models will be made available.