@inproceedings{liang-etal-2021-evaluation,
title = "Evaluation of In-Person Counseling Strategies To Develop Physical Activity Chatbot for Women",
author = "Liang, Kai-Hui and
Lange, Patrick and
Oh, Yoo Jung and
Zhang, Jingwen and
Fukuoka, Yoshimi and
Yu, Zhou",
editor = "Li, Haizhou and
Levow, Gina-Anne and
Yu, Zhou and
Gupta, Chitralekha and
Sisman, Berrak and
Cai, Siqi and
Vandyke, David and
Dethlefs, Nina and
Wu, Yan and
Li, Junyi Jessy",
booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2021",
address = "Singapore and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sigdial-1.5",
doi = "10.18653/v1/2021.sigdial-1.5",
pages = "32--44",
abstract = "Artificial intelligence chatbots are the vanguard in technology-based intervention to change people{'}s behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This work introduces an intervention conversation dataset collected from a real-world physical activity intervention program for women. We designed comprehensive annotation schemes in four dimensions (domain, strategy, social exchange, and task-focused exchange) and annotated a subset of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants{'} changes in the barrier and social support for physical activity. We also analyzed how participant{'}s baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention chatbot.",
}
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<abstract>Artificial intelligence chatbots are the vanguard in technology-based intervention to change people’s behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This work introduces an intervention conversation dataset collected from a real-world physical activity intervention program for women. We designed comprehensive annotation schemes in four dimensions (domain, strategy, social exchange, and task-focused exchange) and annotated a subset of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants’ changes in the barrier and social support for physical activity. We also analyzed how participant’s baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention chatbot.</abstract>
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%0 Conference Proceedings
%T Evaluation of In-Person Counseling Strategies To Develop Physical Activity Chatbot for Women
%A Liang, Kai-Hui
%A Lange, Patrick
%A Oh, Yoo Jung
%A Zhang, Jingwen
%A Fukuoka, Yoshimi
%A Yu, Zhou
%Y Li, Haizhou
%Y Levow, Gina-Anne
%Y Yu, Zhou
%Y Gupta, Chitralekha
%Y Sisman, Berrak
%Y Cai, Siqi
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Wu, Yan
%Y Li, Junyi Jessy
%S Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2021
%8 July
%I Association for Computational Linguistics
%C Singapore and Online
%F liang-etal-2021-evaluation
%X Artificial intelligence chatbots are the vanguard in technology-based intervention to change people’s behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This work introduces an intervention conversation dataset collected from a real-world physical activity intervention program for women. We designed comprehensive annotation schemes in four dimensions (domain, strategy, social exchange, and task-focused exchange) and annotated a subset of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants’ changes in the barrier and social support for physical activity. We also analyzed how participant’s baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention chatbot.
%R 10.18653/v1/2021.sigdial-1.5
%U https://aclanthology.org/2021.sigdial-1.5
%U https://doi.org/10.18653/v1/2021.sigdial-1.5
%P 32-44
Markdown (Informal)
[Evaluation of In-Person Counseling Strategies To Develop Physical Activity Chatbot for Women](https://aclanthology.org/2021.sigdial-1.5) (Liang et al., SIGDIAL 2021)
ACL