Warrant generation through deep learning

Fatima Alkhawaldeh, Tommy Yuan, Dimitar Lubomirov Kazakov

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationSeventh International Conference on Natural Language Computing (NATL 2021), Proc. of
EditorsDavid C. Wyld, et al.
PublisherAIRCC Publishing Corporation
Number of pages23
Publication statusPublished - 27 Nov 2021
EventInternational Conference on Natural Language Computing - London, United Kingdom
Duration: 27 Nov 202128 Feb 2022
Conference number: 7

Publication series

NameComputer Science & Information Technology (CS & IT)
PublisherAIRCC Publishing Corporation


ConferenceInternational Conference on Natural Language Computing
Abbreviated titleNATL 2021
Country/TerritoryUnited Kingdom


  • Toulmin model
  • warrant
  • Fact-checking
  • Deep learning

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