Multi-label Textual Data Augmentation Using BERT Based on Transformer Model

In recent years, the healthcare field has taken a turn towards the AI domain, with applications such as image segmentation and medical report classification leading researchers to delve deeper into this area. On the other hand, the natural language processing has taken another turn with the arrival of the transformer models, the deep learning pre-trained models on a large corpus of documents, permitting an optimal use of medical text documents. However, the lack of diversification in text data more specifically multilabel data can be a constraint to get performant results. In this study, we are utilizing BioBERT for multilabel text classification, with the aim of achieving more refined results by implementing a data augmentation approach using a BERT-based transformer model.