Toward an infrastructure for data-driven multimodal communication research

Toward an infrastructure for data-driven multimodal communication research AbstractResearch into the multimodal dimensions of human communication faces a set of distinctive methodological challenges. Collecting the datasets is resource-intensive, analysis often lacks peer validation, and the absence of shared datasets makes it difficult to develop standards. External validity is hampered by small datasets, yet large datasets are intractable. Red Hen Lab spearheads an international infrastructure for data-driven multimodal communication research, facilitating an integrated cross-disciplinary workflow. Linguists, communication scholars, statisticians, and computer scientists work together to develop research questions, annotate training sets, and develop pattern discovery and machine learning tools that handle vast collections of multimodal data, beyond the dreams of previous researchers. This infrastructure makes it possible for researchers at multiple sites to work in real-time in transdisciplinary teams. We review the vision, progress, and prospects of this research consortium. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Linguistics Vanguard de Gruyter

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Publisher
de Gruyter
Copyright
©2018 Walter de Gruyter GmbH, Berlin/Boston
ISSN
2199-174X
eISSN
2199-174X
D.O.I.
10.1515/lingvan-2017-0041
Publisher site
See Article on Publisher Site

Abstract

AbstractResearch into the multimodal dimensions of human communication faces a set of distinctive methodological challenges. Collecting the datasets is resource-intensive, analysis often lacks peer validation, and the absence of shared datasets makes it difficult to develop standards. External validity is hampered by small datasets, yet large datasets are intractable. Red Hen Lab spearheads an international infrastructure for data-driven multimodal communication research, facilitating an integrated cross-disciplinary workflow. Linguists, communication scholars, statisticians, and computer scientists work together to develop research questions, annotate training sets, and develop pattern discovery and machine learning tools that handle vast collections of multimodal data, beyond the dreams of previous researchers. This infrastructure makes it possible for researchers at multiple sites to work in real-time in transdisciplinary teams. We review the vision, progress, and prospects of this research consortium.

Journal

Linguistics Vanguardde Gruyter

Published: Mar 9, 2018

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