A Composable Generative Framework Based on Prompt Learning for Various Information Extraction Tasks

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 general-purpose framework based on the prompt learning paradigm for various information extraction tasks. In this article, 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 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.

Automated summary

In this article, we propose a novel composable prompt-based generative framework that can be applied to a wide range of tasks in the field of information extraction. By transforming information extraction tasks into the form of filling slots in pre-designed type-specific prompts, we propose a strategy of constructing composable prompts to enhance generalization ability in data-scarce scenarios. Furthermore, relation extraction is transformed into the task of determining semantic consistency in prompts.