Prompt learning is an effective paradigm that bridges gaps between the pre-training tasks and the corresponding downstream applications. Approaches based on this paradigm have achieved great transcendent results in various applications. However, it still needs to be answered how to design a unified framework based on the prompt learning paradigm for various information extraction tasks. In this paper, we propose a novel composable prompt-based generative framework, which could be applied to a wide range of tasks in the field of Information Extraction. Specifically, we reformulate information extraction tasks into the form of filling slots in pre-designed type-specific prompts, which consist of one or multiple sub-prompts. A strategy of constructing composable prompts is proposed to enhance the generalization ability to extract events in data-scarce scenarios. Furthermore, to fit this framework, we transform Relation Extraction into the task of determining semantic consistency in prompts. The experimental results demonstrate that our approach surpasses compared baselines on real-world datasets in data-abundant and data-scarce scenarios. Further analysis of the proposed framework is presented, as well as numerical experiments conducted to investigate impact factors of performance on various tasks.
翻译:快速学习是弥合培训前任务和相应的下游应用之间差距的有效范例。基于这一范例的方法在各种应用中取得了巨大的超常成果。然而,仍然需要回答如何在各种信息提取任务中设计一个基于迅速学习模式的统一框架。在本文件中,我们提出一个新的可折成式的快速基因化框架,可适用于信息提取领域的广泛任务。具体地说,我们重新将信息提取任务改成填补预先设计的具体类型提示的空档的形式,包括一个或多个子程序。提议了一个构建可计算提示的战略,以提高在数据采集情景中提取事件的一般能力。此外,为了适应这一框架,我们将“提取”转换为确定迅速实现语义一致性的任务。实验结果表明,我们的方法超过了数据消耗和数据撕裂情景中真实世界数据集的基准。进一步分析了拟议框架,并进行了数字实验,以调查各种任务绩效的影响。