When Text and Speech are Not Enough: A Multimodal Dataset of Collaboration in a Situated Task

When Text and Speech are Not Enough: A Multimodal Dataset of Collaboration in a Situated Task

To adequately model information exchanged in real human-human interactions, considering speech or text alone leaves out many critical modalities. The channels contributing to the “making of sense” in human-human interactions include but are not limited to gesture, speech, user-interaction modeling,...

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Bibliographic Details
Main Authors: Ibrahim Khebour, Richard Brutti, Indrani Dey, Rachel Dickler, Kelsey Sikes, Kenneth Lai, Mariah Bradford, Brittany Cates, Paige Hansen, Changsoo Jung, Brett Wisniewski, Corbyn Terpstra, Leanne Hirshfield, Sadhana Puntambekar, Nathaniel Blanchard, James Pustejovsky, Nikhil Krishnaswamy
Format: Article
Language:English
Published: Ubiquity Press 2024-01-01
Series:Journal of Open Humanities Data
Subjects:
multimodal interaction
collaboration
problem solving
situated tasks
Online Access:https://account.openhumanitiesdata.metajnl.com/index.php/up-j-johd/article/view/168
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Summary:To adequately model information exchanged in real human-human interactions, considering speech or text alone leaves out many critical modalities. The channels contributing to the “making of sense” in human-human interactions include but are not limited to gesture, speech, user-interaction modeling, gaze, joint attention, and involvement/engagement, all of which need to be adequately modeled to automatically extract correct and meaningful information. In this paper, we present a multimodal dataset of a novel situated and shared collaborative task, with the above channels annotated to encode these different aspects of the situated and embodied involvement of the participants in the joint activity.
ISSN:2059-481X

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