Towards Enhancing Health Coaching Dialogue in Low-Resource Settings
Yue Zhou Barbara Di Eugenio Brian Ziebart Lisa Sharp Bing Liu Ben Gerber Nikolaos Agadakos Shweta Yadav
Health coaching helps patients identify and accomplish lifestyle-related
goals, effectively improving the control of chronic diseases and mitigating
mental health conditions. However, health coaching is cost-prohibitive due to
its highly personalized and labor-intensive nature. In this paper, we propose
to build a dialogue system that converses with the patients, helps them create
and accomplish specific goals, and can address their emotions with empathy.
However, building such a system is challenging since real-world health coaching
datasets are limited and empathy is subtle. Thus, we propose a modularized
health coaching dialogue system with simplified NLU and NLG frameworks combined
with mechanism-conditioned empathetic response generation. Through automatic
and human evaluation, we show that our system generates more empathetic,
fluent, and coherent responses and outperforms the state-of-the-art in NLU
tasks while requiring less annotation. We view our approach as a key step
towards building automated and more accessible health coaching systems.