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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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Goal Summarization for Human-Human Health Coaching Dialogues
Lack of physical activity has been linked to several chronic diseases. Health coaching is successful to help patients engage in healthier behaviors, but is resource intensive. Our goal is to develop a virtual health coach. In this paper, we discuss one component of our work, automatically summarizing goals set by patients during health coaching conversations that we collected and annotated. In turn, our goal summarization pipeline consists of a slot-value prediction model followed by a model that captures the higher-level conversation flow of the dialogues. We report a detailed evaluation that shows measures used for summarization such as BLEU and ROUGE, do not work well for our task.
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- Award ID(s):
- 1838770
- PAR ID:
- 10195187
- Date Published:
- Journal Name:
- The Thirty-Third International FLAIRS Conference (FLAIRS-33)
- Page Range / eLocation ID:
- 317-322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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