Name
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. |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Computational Linguistics | Conference |
Volume | Citations | PageRank |
Proceedings of the 29th International Conference on Computational Linguistics | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Zhou | 1 | 0 | 0.34 |
| Barbara Di Eugenio | 2 | 801 | 109.27 |
| Brian Ziebart | 3 | 0 | 0.34 |
| Lisa K. Sharp | 4 | 18 | 3.74 |
| Bing Liu | 5 | 14486 | 811.80 |
| Ben S. Gerber | 6 | 45 | 7.21 |
| Nikolaos Agadakos | 7 | 0 | 0.68 |
| Shweta Yadav | 8 | 1 | 1.73 |