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.
Cite
@article{arxiv.2404.08888,
title = {Towards Enhancing Health Coaching Dialogue in Low-Resource Settings},
author = {Yue Zhou and Barbara Di Eugenio and Brian Ziebart and Lisa Sharp and Bing Liu and Ben Gerber and Nikolaos Agadakos and Shweta Yadav},
journal= {arXiv preprint arXiv:2404.08888},
year = {2024}
}