论文标题
部分可观测时空混沌系统的无模型预测
Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction
论文作者
论文摘要
关系提取是生物医学领域中自然语言处理的核心问题。关于关系提取的最新研究表明，基于迅速的学习可以提高完整训练集和几次训练的微调方面的表现。但是，在特定于领域的任务上，良好的及时设计可能会更加困难。在本文中，我们研究了提示生物医学关系提取的提示，并在ChemProt数据集上进行了实验。我们提出了一种简单而有效的方法，可以系统地生成全面的提示，以在简单的及时配方中重新将关系提取任务重新制定为固定测试任务。特别是，我们试验不同的排名分数以及时选择。借助Biomed-Roberta-base，我们的结果表明，基于促使基于的基线的促使微调在其常规微调基线上获得了14.21 F1的收益，而Scifive-Large（Chepivive-Large）是ChemProt的当前最新面积。此外，我们发现基于及时的学习需要更少的培训示例来做出合理的预测。结果证明了我们在这种特定领域的关系提取任务中我们方法的潜力。
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.
