A Web-based Tool for Detecting Argument Validity and Novelty

Sandrine Chausson, Ameer Saadat-Yazdi, Xue Li, Jeff Z. Pan, Vaishak Belle, Nadin Kokciyan, Björn Ross

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

Abstract / Description of output

Individuals engage in arguments on an everyday basis as they seek to obtain information about current affairs and engage with social media. While fact-checkers are available to help dispel misinformation, it is almost impossible for users to verify every single claim they encounter. This means that oftentimes, it is left to the user to decide whether a claim is well supported. To address this, we have developed a Web interface that allows users to input an argument, and our developed framework automatically detects its validity (soundness of logical deduction) and novelty (whether the argument is non-circular). Our Web-based tool could be used by social media users who wish to evaluate the information they consume. As part of one of the modules developed at the University of Edinburgh, our tool will be deployed as a teaching tool for the students who study argumentation.
Original languageEnglish
Title of host publicationProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems
Place of PublicationRichland, SC
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
Pages3053–3055
Number of pages3
ISBN (Electronic)9781450394321
DOIs
Publication statusPublished - 30 May 2023
EventThe 22nd International Conference on Autonomous Agents and Multiagent Systems, 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023
Conference number: 22
https://aamas2023.soton.ac.uk/

Conference

ConferenceThe 22nd International Conference on Autonomous Agents and Multiagent Systems, 2023
Abbreviated titleAAMAS 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/232/06/23
Internet address

Keywords / Materials (for Non-textual outputs)

  • computational argumentation
  • neural networks
  • knowledge graphs
  • natural language inference

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