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

Understand

We present a study on the efficacy of adversarial training on transformer neural network models, with respect to the task of detecting check-worthy claims.

• In this work, we introduce the first adversarially-regularized, transformer-based claim spotter model that achieves state-of-the-art results on multiple challenging benchmarks.
• We obtain a 4.70 point F1-score improvement over current state-of-the-art models on the ClaimBuster Dataset and CLEF2019 Dataset, respectively.
• In the process, we propose a method to apply adversarial training to transformer models, which has the potential to be generalized to many similar text classification tasks.