  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. Along with our results, we are
releasing our codebase and manually labeled datasets. We also showcase our
models' real world usage via a live public API.
