  Health mention classification deals with the disease detection in a given
text containing disease words. However, non-health and figurative use of
disease words adds challenges to the task. Recently, adversarial training
acting as a means of regularization has gained popularity in many NLP tasks. In
this paper, we propose a novel approach to train language models for health
mention classification of tweets that involves adversarial training. We
generate adversarial examples by adding perturbation to the representations of
transformer models for tweet examples at various levels using Gaussian noise.
Further, we employ contrastive loss as an additional objective function. We
evaluate the proposed method on the PHM2017 dataset extended version. Results
show that our proposed approach improves the performance of classifier
significantly over the baseline methods. Moreover, our analysis shows that
adding noise at earlier layers improves models' performance whereas adding
noise at intermediate layers deteriorates models' performance. Finally, adding
noise towards the final layers performs better than the middle layers noise
addition.
