Constructing artificial data for fine-tuning for low-resource biomedical text tagging with applications in pico annotation

Biomedical text tagging systems are plagued by the dearth of labeled training data. There have been recent attempts at using pre-trained encoders to deal with this issue. Pre-trained encoder provides representation of the input text which is then fed to task-specific layers for classification. The entire network is fine-tuned on the labeled data from the target task. Unfortunately, a low-resource biomedical task often has too few labeled instances for satisfactory fine-tuning. Also, if the label space is large, it contains few or no labeled instances for majority of the labels. Most biomedical tagging systems treat labels as indexes, ignoring the fact that these labels are often concepts expressed in natural language e.g. ‘Appearance of lesion on brain imaging’. To address these issues, we propose constructing extra labeled instances using label-text (i.e. label’s name) as input for the corresponding label-index (i.e. label’s index …