@inproceedings{gupta-etal-2020-human,
title = "Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis",
author = "Gupta, Itika and
Di Eugenio, Barbara and
Ziebart, Brian and
Baiju, Aiswarya and
Liu, Bing and
Gerber, Ben and
Sharp, Lisa and
Nabulsi, Nadia and
Smart, Mary",
editor = "Pietquin, Olivier and
Muresan, Smaranda and
Chen, Vivian and
Kennington, Casey and
Vandyke, David and
Dethlefs, Nina and
Inoue, Koji and
Ekstedt, Erik and
Ultes, Stefan",
booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2020",
address = "1st virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sigdial-1.30",
doi = "10.18653/v1/2020.sigdial-1.30",
pages = "246--256",
abstract = "Our goal is to develop and deploy a virtual assistant health coach that can help patients set realistic physical activity goals and live a more active lifestyle. Since there is no publicly shared dataset of health coaching dialogues, the first phase of our research focused on data collection. We hired a certified health coach and 28 patients to collect the first round of human-human health coaching interaction which took place via text messages. This resulted in 2853 messages. The data collection phase was followed by conversation analysis to gain insight into the way information exchange takes place between a health coach and a patient. This was formalized using two annotation schemas: one that focuses on the goals the patient is setting and another that models the higher-level structure of the interactions. In this paper, we discuss these schemas and briefly talk about their application for automatically extracting activity goals and annotating the second round of data, collected with different health coaches and patients. Given the resource-intensive nature of data annotation, successfully annotating a new dataset automatically is key to answer the need for high quality, large datasets.",
}
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<abstract>Our goal is to develop and deploy a virtual assistant health coach that can help patients set realistic physical activity goals and live a more active lifestyle. Since there is no publicly shared dataset of health coaching dialogues, the first phase of our research focused on data collection. We hired a certified health coach and 28 patients to collect the first round of human-human health coaching interaction which took place via text messages. This resulted in 2853 messages. The data collection phase was followed by conversation analysis to gain insight into the way information exchange takes place between a health coach and a patient. This was formalized using two annotation schemas: one that focuses on the goals the patient is setting and another that models the higher-level structure of the interactions. In this paper, we discuss these schemas and briefly talk about their application for automatically extracting activity goals and annotating the second round of data, collected with different health coaches and patients. Given the resource-intensive nature of data annotation, successfully annotating a new dataset automatically is key to answer the need for high quality, large datasets.</abstract>
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%0 Conference Proceedings
%T Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis
%A Gupta, Itika
%A Di Eugenio, Barbara
%A Ziebart, Brian
%A Baiju, Aiswarya
%A Liu, Bing
%A Gerber, Ben
%A Sharp, Lisa
%A Nabulsi, Nadia
%A Smart, Mary
%Y Pietquin, Olivier
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Kennington, Casey
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Inoue, Koji
%Y Ekstedt, Erik
%Y Ultes, Stefan
%S Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2020
%8 July
%I Association for Computational Linguistics
%C 1st virtual meeting
%F gupta-etal-2020-human
%X Our goal is to develop and deploy a virtual assistant health coach that can help patients set realistic physical activity goals and live a more active lifestyle. Since there is no publicly shared dataset of health coaching dialogues, the first phase of our research focused on data collection. We hired a certified health coach and 28 patients to collect the first round of human-human health coaching interaction which took place via text messages. This resulted in 2853 messages. The data collection phase was followed by conversation analysis to gain insight into the way information exchange takes place between a health coach and a patient. This was formalized using two annotation schemas: one that focuses on the goals the patient is setting and another that models the higher-level structure of the interactions. In this paper, we discuss these schemas and briefly talk about their application for automatically extracting activity goals and annotating the second round of data, collected with different health coaches and patients. Given the resource-intensive nature of data annotation, successfully annotating a new dataset automatically is key to answer the need for high quality, large datasets.
%R 10.18653/v1/2020.sigdial-1.30
%U https://aclanthology.org/2020.sigdial-1.30
%U https://doi.org/10.18653/v1/2020.sigdial-1.30
%P 246-256
Markdown (Informal)
[Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis](https://aclanthology.org/2020.sigdial-1.30) (Gupta et al., SIGDIAL 2020)
ACL
- Itika Gupta, Barbara Di Eugenio, Brian Ziebart, Aiswarya Baiju, Bing Liu, Ben Gerber, Lisa Sharp, Nadia Nabulsi, and Mary Smart. 2020. Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 246–256, 1st virtual meeting. Association for Computational Linguistics.