Jackylyn L. Beredo, Ethel C. Ong

Analyzing the Capabilities of a Hybrid Response Generation Model for an Empathetic Conversational Agent

  • General Earth and Planetary Sciences
  • General Engineering
  • General Environmental Science
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Research on the use of conversational agents or chatbots to provide alternative and accessible mental health interventions has gained much interest in recent years. Designed to engage human users through natural and empathetic conversations, these chatbots have shown their potential applications in pre-emptive healthcare that emphasizes the importance of helping individuals maintain their optimal mental health and well-being. The ability of chatbots to process user input and produce pertinent and sympathetic responses to dynamically adjust to the context of a discussion is, however, constrained by the usage of retrieval-based models. Neural-based generative models, currently applied in open-domain dialogue and text generation systems, may be able to address the limitations of retrieval-based models. In this paper, we present Virtual Hope (VHope), a conversational agent that combines retrieval-based and generative models to perform its role as a therapist capable of generating empathetic responses to enrich the conversation. The best performing generative model, derived from training DialoGPT with the EmpatheticDialogues dataset and a local mental well-being dataset, yielded a perplexity score of 9.977. Two variations for embedding retrieval and generative models in the chatbot’s conversation flow yielded insignificant differences based on users’ evaluation of VHope’s performance. Results from experts’ analysis of the conversation logs showed that the responses generated by VHope were 67% relevant, 78% human-like, and 79% empathic. Future improvements may include the use of a larger, human-based empathetic dataset for enhanced retrieval model’s conversation design and generative model’s fine-tuning.

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