  Relation extraction is a core problem for natural language processing in the
biomedical domain. Recent research on relation extraction showed that
prompt-based learning improves the performance on both fine-tuning on full
training set and few-shot training. However, less effort has been made on
domain-specific tasks where good prompt design can be even harder. In this
paper, we investigate prompting for biomedical relation extraction, with
experiments on the ChemProt dataset. We present a simple yet effective method
to systematically generate comprehensive prompts that reformulate the relation
extraction task as a cloze-test task under a simple prompt formulation. In
particular, we experiment with different ranking scores for prompt selection.
With BioMed-RoBERTa-base, our results show that prompting-based fine-tuning
obtains gains by 14.21 F1 over its regular fine-tuning baseline, and 1.14 F1
over SciFive-Large, the current state-of-the-art on ChemProt. Besides, we find
prompt-based learning requires fewer training examples to make reasonable
predictions. The results demonstrate the potential of our methods in such a
domain-specific relation extraction task.
