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.

本文探讨在生物医学领域中的关系抽取问题，研究使用集成学习的方式能否提高在 ChemProt 数据集上的预测结果，结果展示了基于掩码的抽取方法有潜力在该特定领域关系抽取任务中取得优异的性能。