Development and Evaluation of Three Chatbots for Postpartum Mood and
  Anxiety Disorders
Xuewen Yao Miriam Mikhelson S. Craig Watkins Eunsol Choi Edison Thomaz Kaya de Barbaro
In collaboration with Postpartum Support International (PSI), a non-profit
organization dedicated to supporting caregivers with postpartum mood and
anxiety disorders, we developed three chatbots to provide context-specific
empathetic support to postpartum caregivers, leveraging both rule-based and
generative models. We present and evaluate the performance of our chatbots
using both machine-based metrics and human-based questionnaires. Overall, our
rule-based model achieves the best performance, with outputs that are close to
ground truth reference and contain the highest levels of empathy. Human users
prefer the rule-based chatbot over the generative chatbot for its
context-specific and human-like replies. Our generative chatbot also produced
empathetic responses and was described by human users as engaging. However,
limitations in the training dataset often result in confusing or nonsensical
responses. We conclude by discussing practical benefits of rule-based vs.
generative models for supporting individuals with mental health challenges. In
light of the recent surge of ChatGPT and BARD, we also discuss the
possibilities and pitfalls of large language models for digital mental
healthcare.