Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training

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.