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

Hui-Syuan Yeh, Thomas Lavergne, Pierre Zweigenbaum


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
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3780–3787
Language:
URL:
https://aclanthology.org/2022.lrec-1.403
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.403.pdf