If the Shoe Fits: Towards A Conceptual Model for Applied Deep Learning in Social Computing
Speaker
Carolyn P Rose
Professor, Human-Computer Interaction Institute, Carnegie Mellon University
When
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Where
Newell-Simon Hall 1305 (Michael Mauldin Auditorium)
Video
Video link
Description
For more than a decade, a growing interest in automated processing of behavior traces has been in evidence across areas in HCI, perhaps especially in Social Computing. Each new wave in computational modeling paradigms raises hopes of new possibilities, most recently Deep Learning. This talk presents both a hopeful and critical look at Deep Learning in terms of what it offers the field of HCI, especially in the area of Social Computing, and articulates a conceptual model for thinking about how to wield it in a reflective way. As a focal example, in this talk we probe into a specific quality of discussion referred to as Transactivity. Transactivity is the extent to which a contribution articulates the reasoning of the speaker, that of an interlocutor, and the relation between them. In different contexts, and within very distinct theoretical frameworks, this construct has been associated with solidarity, influence, expertise transfer, and learning. From a cognitive perspective, what is important about Transactivity is the concept of collaborative knowledge integration and idea building. To capture this computationally we build on the linguistic concept of entailment and explore how this concept motivates a transfer learning approach within a Deep Learning paradigm. Both successes and caveats will be discussed.
Speaker's Bio
Dr. Carolyn Rosé is a Professor of Language Technologies and Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University. Her research program is focused on better understanding the social and pragmatic nature of conversation, and using this understanding to build computational systems that can improve the efficacy of conversation between people, and between people and computers.
In order to pursue these goals, she invokes approaches from computational discourse analysis and text mining, conversational agents, and computer supported collaborative learning. Her research group’s highly interdisciplinary work, published in over 200 peer reviewed publications, is represented in the top venues in 5 fields: namely, Language Technologies, Learning Sciences, Cognitive Science, Educational Technology, and Human-Computer Interaction, with awards in 3 of these fields. She serves as Past President and Inaugural Fellow of the International Society of the Learning Sciences, Chair of the International Alliance to Advance Learning in the Digital Era, and Executive Editor of the International Journal of Computer-Supported Collaborative Learning.