Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal
  Summarization and Text-Units-Text Generation
Yue Zhou Barbara Di Eugenio Brian Ziebart Lisa Sharp Bing Liu Nikolaos Agadakos
Health coaching helps patients achieve personalized and lifestyle-related
goals, effectively managing chronic conditions and alleviating mental health
issues. It is particularly beneficial, however cost-prohibitive, for
low-socioeconomic status populations due to its highly personalized and
labor-intensive nature. In this paper, we propose a neuro-symbolic goal
summarizer to support health coaches in keeping track of the goals and a
text-units-text dialogue generation model that converses with patients and
helps them create and accomplish specific goals for physical activities. Our
models outperform previous state-of-the-art while eliminating the need for
predefined schema and corresponding annotation. We also propose a new health
coaching dataset extending previous work and a metric to measure the
unconventionality of the patient's response based on data difficulty,
facilitating potential coach alerts during deployment.