Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims-Reference-Cited by-同舟云学术

Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims

Published:2024-08-20 Issue: Volume: Page:
ISSN:2157-6904
Container-title:ACM Transactions on Intelligent Systems and Technology
language:en
Short-container-title:ACM Trans. Intell. Syst. Technol.

Author:

Meng Kevin¹^ORCID,Jimenez Damian²^ORCID,Devasier Jacob Daniel²^ORCID,Naraparaju Sai Sandeep²^ORCID,Arslan Fatma²^ORCID,Obembe Daniel²^ORCID,Li Chengkai²^ORCID

Affiliation:

1. Massachusetts Institute of Technology, United States

2. The University of Texas at Arlington, United States

Abstract

This paper presents the latest developments to ClaimBuster's claim-spotting model, which tackles the critical task of identifying check-worthy claims from large streams of information. We introduce the first adversarially-regularized, transformer-based claim-spotting model, which achieves state-of-the-art results on several benchmark datasets. In addition to analyzing model performance metrics, we also quantitatively and qualitatively analyze the impact of ClaimBuster's real-world deployment. Moreover, to help facilitate reproducibility and community engagement, we publicly release our codebase, dataset, data curation platform, API, Google Colab notebooks, and various ClaimBuster-based demo systems, at claimbuster.org.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3689212

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