论文标题
Health Prompt：用于临床自然语言处理的零拍学习范例
HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing
论文作者
论文摘要
深度学习算法取决于大规模注释的临床文本数据集的可用性。缺乏这样的公开数据集是开发临床自然语言处理（NLP）系统的最大瓶颈。零射门学习（ZSL）是指使用深度学习模型从未见过培训数据的新类别中对实例进行分类。基于及时的学习是一种新兴的ZSL技术，我们为NLP任务定义了基于任务的模板。我们开发了一个新型的基于及时的临床NLP框架，称为Health Prompt，并在临床文本上应用了基于及时的学习范式。在此技术中，而不是微调预训练的语言模型（PLM），而是通过定义及时模板来调整任务定义。在NO-DATA环境中，我们对六个不同的PLM的Health Prompt进行了深入分析。我们的实验证明了提示有效捕获临床文本的上下文，并且在没有任何培训数据的情况下表现出色。
Deep learning algorithms are dependent on the availability of large-scale annotated clinical text datasets. The lack of such publicly available datasets is the biggest bottleneck for the development of clinical Natural Language Processing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deep learning models to classify instances from new classes of which no training data have been seen before. Prompt-based learning is an emerging ZSL technique where we define task-based templates for NLP tasks. We developed a novel prompt-based clinical NLP framework called HealthPrompt and applied the paradigm of prompt-based learning on clinical texts. In this technique, rather than fine-tuning a Pre-trained Language Model(PLM), the task definitions are tuned by defining a prompt template. We performed an in-depth analysis of HealthPrompt on six different PLMs in a no-data setting. Our experiments prove that prompts effectively capture the context of clinical texts and perform remarkably well without any training data.
