  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.
