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


Abstract
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 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.
Anthology ID:
2022.coling-1.58
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
694–706
Language:
URL:
https://aclanthology.org/2022.coling-1.58
DOI:
Bibkey:
Cite (ACL):
Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben Gerber, Nikolaos Agadakos, and Shweta Yadav. 2022. Towards Enhancing Health Coaching Dialogue in Low-Resource Settings. In Proceedings of the 29th International Conference on Computational Linguistics, pages 694–706, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings (Zhou et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.58.pdf