Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction

Hui-Syuan Yeh (1) , Thomas Lavergne (1) , Pierre Zweigenbaum (1)

1 ILES - Information, Langue Ecrite et Signée

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. 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 domainspecific relation extraction task.

Mots clés: Natural Language Processing; Relation Extraction; Prompting; Language Models

Domaines: Traitement du texte et du document; Informatique et langage [cs.CL]

Hui-Syuan Yeh, Thomas Lavergne, Pierre Zweigenbaum. Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction. LREC 2022 - Language Resources and Evaluation Conference, Jun 2022, Marseille, France. pp.3780-3787. ⟨hal-03867421⟩