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.
Original language | English |
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Title of host publication | Seventh International Conference on Natural Language Computing (NATL 2021), Proc. of |
Editors | David C. Wyld, et al. |
Publisher | AIRCC Publishing Corporation |
Pages | 53-75 |
Number of pages | 23 |
DOIs | |
Publication status | Published - 27 Nov 2021 |
Event | International Conference on Natural Language Computing - London, United Kingdom Duration: 27 Nov 2021 → 28 Feb 2022 Conference number: 7 |
Publication series
Name | Computer Science & Information Technology (CS & IT) |
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Publisher | AIRCC Publishing Corporation |
Conference
Conference | International Conference on Natural Language Computing |
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Abbreviated title | NATL 2021 |
Country/Territory | United Kingdom |
City | London |
Period | 27/11/21 → 28/02/22 |
Keywords
- Toulmin model
- warrant
- Fact-checking
- Deep learning