Alkhawaldeh, Fatima, Yuan, Tommy and Kazakov, Dimitar Lubomirov orcid.org/0000-0002-0637-8106 (2021) Warrant generation through deep learning. In: Wyld, David C. and al., et, (eds.) Seventh International Conference on Natural Language Computing (NATL 2021), Proc. of. International Conference on Natural Language Computing, 27 Nov 2021 - 28 Feb 2022 Computer Science & Information Technology (CS & IT) . AIRCC Publishing Corporation , GBR , pp. 53-75.
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. Despite the critical role warrants play in facilitating argument comprehension, the fact that most works aim to select the best warrant from existing structured data and labelled data is scarce presents a fact-checking challenge, particularly when the evidence is insufficient, or the conclusion is not inferred or generated well based on the evidence. Additionally, deep learning methods for false information detection face a significant bottleneck due to their training requirement of a large amount of labelled data. 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Keywords: | Toulmin model,warrant,Fact-checking,Deep learning |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 08 Feb 2022 10:00 |
Last Modified: | 01 Dec 2024 00:55 |
Published Version: | https://doi.org/10.5121/csit.2021.112005 |
Status: | Published |
Publisher: | AIRCC Publishing Corporation |
Series Name: | Computer Science & Information Technology (CS & IT) |
Identification Number: | 10.5121/csit.2021.112005 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183371 |
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