  Health mentioning classification (HMC) classifies an input text as health
mention or not. Figurative and non-health mention of disease words makes the
classification task challenging. Learning the context of the input text is the
key to this problem. The idea is to learn word representation by its
surrounding words and utilize emojis in the text to help improve the
classification results. In this paper, we improve the word representation of
the input text using adversarial training that acts as a regularizer during
fine-tuning of the model. We generate adversarial examples by perturbing the
embeddings of the model and then train the model on a pair of clean and
adversarial examples. Additionally, we utilize contrastive loss that pushes a
pair of clean and perturbed examples close to each other and other examples
away in the representation space. We train and evaluate the method on an
extended version of the publicly available PHM2017 dataset. Experiments show an
improvement of 1.0% over BERT-Large baseline and 0.6% over RoBERTa-Large
baseline, whereas 5.8% over the state-of-the-art in terms of F1 score.
Furthermore, we provide a brief analysis of the results by utilizing the power
of explainable AI.
