Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction
Hui-Syuan Yeh, Thomas Lavergne, Pierre Zweigenbaum · 2022
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.