论文标题
提示结合术语：教授预训练的模型以了解稀有生物医学单词
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
论文作者
论文摘要
事实证明，基于预训练的模型的迅速基于基于预训练的模型对许多自然语言处理任务有效，在一般域中的几个设置下。但是，尚未对生物医学领域进行迅速调整。生物医学单词在一般领域通常很少见，但在生物医学环境中无处不在，这在微观的生物医学应用上显着恶化了预训练的模型的性能，即使在微调之后，尤其是在低资源场景中。我们提出了一种简单而有效的方法，可以帮助模型在迅速调整过程中学习稀有的生物医学单词。实验结果表明，我们的方法可以使用少量的香草提示设置，无需任何额外的参数或培训步骤即可提高生物医学自然推理任务6％。
Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prompt in biomedical domain has not been investigated thoroughly. Biomedical words are often rare in general domain, but quite ubiquitous in biomedical contexts, which dramatically deteriorates the performance of pre-trained models on downstream biomedical applications even after fine-tuning, especially in low-resource scenarios. We propose a simple yet effective approach to helping models learn rare biomedical words during tuning with prompt. Experimental results show that our method can achieve up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings.
