  The massive scale and growth of textual biomedical data have made its
indexing and classification increasingly important. However, existing research
on this topic mainly utilized convolutional and recurrent neural networks,
which generally achieve inferior performance than the novel transformers. On
the other hand, systems that apply transformers only focus on the target
documents, overlooking the rich semantic information that label descriptions
contain. To address this gap, we develop a transformer-based biomedical text
classifier that considers label information. The system achieves this with a
label attention module incorporated into the fine-tuning process of pretrained
language models (PTMs). Our results on two public medical datasets show that
the proposed fine-tuning scheme outperforms the vanilla PTMs and
state-of-the-art models.
