Details:

Journal Title:
Neural Computing and Applications
Personal Author:
Pugh, Katrina ; Musavi, Mohamad ; Johnson, Teresa ; Burke, Christopher ; Yoeli, Erez ; ... More +
Pugh, Katrina ; Musavi, Mohamad ; Johnson, Teresa ; Burke, Christopher ; Yoeli, Erez ; Currie, Emily ; Pugh, Benjamin Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research)
Description:
We live in the age polarization, where conversations on matters of sustainability more often produce acrimony or stalemate than productive action. Better understanding conversation features and their impacts may lead to better innovation, solution-design, and ongoing collaboration. We describe a study to test alternate machine learning models for classifying six “discussion disciplines”, which are conversation features associated with rhetorical intent. The model providing the best outcome used the Bi-directional Encoder Representations from Transformers (BERT) layered with a Residual Network (ResNet). The training data were 1135 utterances from Maine aquaculture town hall-like meetings and similar conversations, which had been hand-coded for the discussion disciplines. In addition, we generated 300 phrases corresponding to three conversation outcomes: Intent-to-Act, Options-Generation, and Relationship-Building. We then used the trained model and information retrieval to classify a large corpus of 591 open-source transcripts, containing over 21,000 utterances. A binary logistic regression analysis showed that two discussion disciplines, “Inclusion” and “Courtesy,” had positive, statistically significant, impacts on Intent-to-act: a 10 percentage point increase in the share of the Inclusion or Courtesy yielded a 45% or 34% increase, respectively, in the likelihood of Intent-to-Act. This study shows the applicability of neural networks in modeling conversations and identifying the dialog acts that can provide measurable and predictable impact on conversation outcomes. Conversational intelligence can support a variety of human interactions, such as town halls, policy-deliberations, private–public partnerships, and sustainability teamwork.
Keywords:
[+]
Artificial Intelligence Software
Source:
Neural Computing and Applications, 35(29), 21935-21947
DOI:
https://doi.org/10.1007/s00521-023-08819-z
ISSN:
0941-0643;1433-3058;
Format:
PDF
Publisher:
Springer Science and Business Media LLC
Document Type:
Journal Article
Funding:
Grant no. NA18OAR4170103
License:
https://creativecommons.org/licenses/by/4.0/
Rights Information:
CC BY
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:cfb8ef947ccc8c31315c62f10b6d0d45a92ad5cf01f7afa99c517d66988e75ae
Download URL:
https://repository.library.noaa.gov/view/noaa/58631/noaa_58631_DS1.pdf
File Type:

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