Abstract
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.- Anthology ID:
- 2022.lrec-1.403
- Original:
- 2022.lrec-1.403v1
- Version 2:
- 2022.lrec-1.403v2
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 3780–3787
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.403
- DOI:
- Cite (ACL):
- Hui-Syuan Yeh, Thomas Lavergne, and Pierre Zweigenbaum. 2022. Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3780–3787, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction (Yeh et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.lrec-1.403.pdf