Fine-tuning Pretrained Language Models with Label Attention for Explainable Biomedical Text Classification

The massive growth of digital biomedical data is making biomedical text indexing and classification increasingly important. Accordingly, previous research has devised numerous techniques ranging from rule-based systems to deep neural networks, with most focusing on feedforward, convolutional or recurrent neural architectures. More recently, fine-tuned transformers-based pretrained models (PTMs) have demonstrated superior performance in many natural language processing tasks. However, the direct use of PTMs in the biomedical domain is only limited to the target documents, ignoring the rich semantic information in the label descriptions. In this paper, we develop an improved label attention-based architecture to inject semantic label description into the fine-tuning process of PTMs. Results on two public medical datasets show that the proposed fine-tuning scheme outperforms the conventionally fine-tuned PTMs and prior state-of-the-art models. Furthermore, we show that fine-tuning with the label attention mechanism is interpretable in the interpretability study.