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7th International Conference on Natural Language Computing (NATL 2021), November 27~28, 2021, London, United Kingdom
Volume Editors : David C. Wyld, Dhinaharan Nagamalai (Eds)
ISBN : 978-1-925953-54-1
Volume 11, Number 20, November 2021
Warrant Generation through Deep Learning
Fatima T. Alkhawaldeh, Tommy Yuan and Dimitar Kazakov, University of York, UK
Abstract
The warrant element of the Toulmin model is critical for fact-checking and assessing the strength of an argument. As implicit information, warrants justify the arguments and explain why the evidence supports the claim. Manually annotating data, on the other hand, is a time-consuming and laborious process. Thus, we examine the extent to which warrants can be retrieved or reconfigured using unstructured data obtained from their premises. Keywords Toulmin model, warrant, fact-checking, and deep learning.
https://www.youtube.com/watch?v=wVYw0FL 1e8&ab_channel=ComputerScience%26ITConferenceProceedings
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