Improving Team's Consistency of Understanding in Meetings

Kim, Joseph; Shah, Julie A.

doi:10.1109/thms.2016.2547186

Cited by 16 publications

(11 citation statements)

References 48 publications

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“…We construct features from our model predictions to capture different discourse patterns and word entrainment scores for discussion with different COU level. Results on AMI discussions show that SVM classifiers trained with our features significantly outperform the state-ofthe-art results (Kim and Shah, 2016) (F1: 63.1 vs. 50.5) and non-trivial baselines.…”

Section: Uncertain Optionmentioning

confidence: 87%

“…All SVMs trained with our features surpass the ngrams-based baseline. Especially, the discourse features, word entrainment feature, and the combination of the three, all significantly outperform the state-of-theart system by Kim and Shah (2016). 6…”

Section: Predicting Consistency Of Understandingmentioning

confidence: 98%

“…As discussed in previous work (Mulder et al, 2002;Mercer, 2004), both content and discourse structure are critical for building shared understanding among discussants. In this section, we test whether our joint model can be utilized to predict the consistency among team members' under-standing of their group decisions, which is defined as consistency of understanding (COU) in Kim and Shah (2016). Kim and Shah (2016) establish gold-standard COU labels on a portion of AMI discussions, by comparing participant summaries to determine whether participants report the same decisions.…”

Section: Predicting Consistency Of Understandingmentioning

confidence: 99%

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Joint Modeling of Content and Discourse Relations in Dialogues

Qin

Wang

Kim

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent variables. Experimental results on two popular meeting corpora show that our joint model can outperform state-of-the-art approaches for both phrasebased content selection and discourse relation prediction tasks. We also evaluate our model on predicting the consistency among team members' understanding of their group decisions. Classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art.

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Section: Uncertain Optionmentioning

confidence: 87%

Section: Predicting Consistency Of Understandingmentioning

confidence: 98%

Section: Predicting Consistency Of Understandingmentioning

confidence: 99%

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Joint Modeling of Content and Discourse Relations in Dialogues

Qin

Wang

Kim

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

Self Cite

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show abstract

“…Constructing a generalized system requires a few primary components or building blocks. In our previous work, we have identified six major components of an HMT, with emphasis on architectures, interfaces, and metrics based on various literature [36,[49][50][51][52][53][54][55][56][57][58][59][60][61]. These are defined in details in our previous work [4].…”

Section: Hmt Architecture Designmentioning

confidence: 99%

Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human-Machine Teams (HMT)

Damacharla¹,

Dhakal²,

Bandreddi³

et al. 2020

Preprint

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This paper presents a multi-year research study conducted at the University of Toledo, aimed at improving human-machine teaming (HMT) methods and technologies. With the incorporation of artificial intelligence (AI) into 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened a path for the new area of HMT. When it comes to crucial tasks such as patient treatment and care, defense, and industrial production, the use of non-standardized HMT technologies may pose a risk to human lives as well as up to billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we aim to address the lack of HMT design and development standards. We propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and human team. Followed by processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method using two controlled HMT scenarios; first, emergency care provider (ECP) training, and second, patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. As part of these HMT simulation studies, the impact of each parameter related to machines, humans, and HMT (such as errors made by humans and by the VBSA) is presented from the perspective of performance, rules, roles, and operation limitations. In addition, based on the analysis of results from two case studies, we presented guidelines for designing future HMT. Our conclusions indicate that the proposed HIL can be used to perform standardization studies in the pursuit of developing techniques for benchmarking HMT that can be used in critical situations.

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“…The visual summary can also be used to extract abstract insights about this one meeting, or a set of similar meetings together and allows for agents that may have missed the meeting to catch up on the proceedings. Whilst merely sampling the visualization at discrete time-intervals serves as a powerful tool towards automated summary generation, we anticipate the use of more sophisticated visualization [21] and summarization [22][23][24] techniques in the future.…”

Section: Automated Meeting Summarizationmentioning

confidence: 99%

Planning and visualization for a smart meeting room assistant1

Chakraborti

Fadnis

Talamadupula

et al. 2019

AIC

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In this paper, we report on the planning and visualization capabilities of Mr.Jones -a proactive orchestrator and decision-support agent for a collaborative decision making setting embodied by a smart room. The duties of such an agent may range across interactive problem solving with other agents in the environment, developing automated summaries of meetings, visualization of the internal decision-making process, proactive data and resource management, and so on. Specifically, we focus on how the visualization of the planning and plan recognition processes forms a key component of the smart assistant, and establishes transparency in the decision-making process. We also highlight how these processes contribute to the proactive nature of the agent. We demonstrate some of these functionalities in a successful deployment of the system in the CEL -the Cognitive Environments Laboratory at IBM's T.J. Watson Research Center (Yorktown, USA), and report on emerging deployments of the system that have turned into success stories.

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Improving Team's Consistency of Understanding in Meetings

Cited by 16 publications

References 48 publications

Joint Modeling of Content and Discourse Relations in Dialogues

Joint Modeling of Content and Discourse Relations in Dialogues

Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human-Machine Teams (HMT)

Planning and visualization for a smart meeting room assistant1

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