BLISS: An Agent for Collecting Spoken Dialogue Data about Health and Well-being

Jelte van Waterschoot, Iris Hendrickx, Arif Khan, Esther Klabbers, Marcel de Korte, Helmer Strik, Catia Cucchiarini, Mariët Theune

Abstract
An important objective in health-technology is the ability to gather information about people’s well-being. Structured interviews can be used to obtain this information, but are time-consuming and not scalable. Questionnaires provide an alternative way to extract such information, though typically lack depth. In this paper, we present our first prototype of the BLISS agent, an artificial intelligent agent which intends to automatically discover what makes people happy and healthy. The goal of Behaviour-based Language-Interactive Speaking Systems (BLISS) is to understand the motivations behind people’s happiness by conducting a personalized spoken dialogue based on a happiness model. We built our first prototype of the model to collect 55 spoken dialogues, in which the BLISS agent asked questions to users about their happiness and well-being. Apart from a description of the BLISS architecture, we also provide details about our dataset, which contains over 120 activities and 100 motivations and is made available for usage.
Anthology ID:
2020.lrec-1.57
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
449–458
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.57
DOI:
Bibkey:
Cite (ACL):
Jelte van Waterschoot, Iris Hendrickx, Arif Khan, Esther Klabbers, Marcel de Korte, Helmer Strik, Catia Cucchiarini, and Mariët Theune. 2020. BLISS: An Agent for Collecting Spoken Dialogue Data about Health and Well-being. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 449–458, Marseille, France. European Language Resources Association.
Cite (Informal):
BLISS: An Agent for Collecting Spoken Dialogue Data about Health and Well-being (van Waterschoot et al., LREC 2020)
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