Towards Enhancing Health Coaching Dialogue in Low-Resource Settings
<|reference_start|>Towards Enhancing Health Coaching Dialogue in Low-Resource Settings: 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.<|reference_end|>
@article{zhou2024towards,
    title={Towards Enhancing Health Coaching Dialogue in Low-Resource Settings},
    author={Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben
  Gerber, Nikolaos Agadakos and Shweta Yadav},
    journal={arXiv preprint arXiv:2404.08888},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08888},
    primaryClass={cs.CL cs.LG}
}