To further explore possibilities of data augmentation during model training, an adversarial training approach was implemented, aiming to increase the model generalisability and robustness to unseen examples. Adversarial training assumes perturbation of the model input based on the calculated gradients. However, the direct perturbation of BERT inputs is generally not feasible, and the calculated embeddings (𝑥 𝑒𝑚𝑏𝑒𝑑 ) need to be augmented instead. Our implementation of BERT adversarial training is similar to previous reports, but the embedding perturbation is done dynamically, by scaling the values by a hyperparameter, instead of adding a pre-specified scalar. We believe that this is a more appropriate and potentially stable solution when dealing with embeddings which can contain a range of values of different magnitude.