Abstract
Socially Assistive Robots are becoming essential in the field of elderly care, as they can support caregivers in their tasks, for instance, by providing senior users with emotional and psychological support through verbal communication. In this paper, we present the results of a project where we developed an interactive dialogue system so that a robot could engage elderly users in conversations about their personal life stories. A task that seems almost mundane for the average person, is in fact extremely challenging for a machine to achieve. Through the development of a comprehensive platform with a variety of modules, the system is able to extract essential keywords from a user utterance and classify them according to sentence context and word meaning. These keywords are then indexed in a user-specific knowledge base, where semantic associations between items are made, relating them, for instance, by time or place. These items are used by the robot to generate responses to the user’s speech by leveraging a hybrid template/data-driven mechanism. As the user interacts with the system, it learns more details which further enrich the generated sentences. The system was evaluated on a human-in-loop experiment, where the results showed the ability of the system to understand human speech, memorize personal information of each user and generate coherent responses in dialogic interactions. These results highlight the potential of a robot not only to provide companionship, but also to build a social relationship with its user.
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Hsiao, YT., Gamborino, E., Fu, LC. (2020). A Hybrid Conversational Agent with Semantic Association of Autobiographic Memories for the Elderly. In: Rau, PL. (eds) Cross-Cultural Design. Applications in Health, Learning, Communication, and Creativity. HCII 2020. Lecture Notes in Computer Science(), vol 12193. Springer, Cham. https://doi.org/10.1007/978-3-030-49913-6_5
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