NDLI: Automatic prediction of consistency among team members' understanding of group decisions in meetings

Content Provider	IEEE Xplore Digital Library
Author	Kim, J. Shah, J.A.
Copyright Year	2014
Description	Author affiliation: Dept. of Aeronaut. & Astronaut., Massachusetts Inst. of Technol., Cambridge, MA, USA (Kim, J.; Shah, J.A.)
Abstract	Occasionally, participants in a meeting can leave with different understandings of what had been discussed. For meetings that require immediate response (such as disaster response planning), the participants must share a common understanding of the decisions reached by the group to ensure successful execution of their mission. In such domains, inconsistency among individuals' understanding of the meeting results would be detrimental, as this can potentially degrade group performance. Thus, detecting the occurrence of inconsistencies in understanding among meeting participants is a desired capability for an intelligent system that would monitor meetings and provide feedback to spur stronger group understanding. In this paper, we seek to predict the consistency among team members' understanding of group decisions. We use self-reported summaries as a representative measure for team members' understanding following meetings, and present a computational model that uses a set of verbal and nonverbal features from natural dialogue. This model focuses on the conversational dynamics between the participants, rather than on what is being discussed. We apply our model to a real-world conversational dataset and show that its features can predict group consistency with greater accuracy than conventional dialogue features. We also show that the combination of verbal and nonverbal features in multimodal fusion improves several performance metrics, and that our results are consistent across different meeting phases.
Starting Page	3702
Ending Page	3708
File Size	214223
Page Count	7
File Format	PDF
ISBN	9781479938407
DOI	10.1109/SMC.2014.6974506
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Computational modeling Decision making Predictive models Joints Monitoring Accuracy
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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