Information Extraction, which aims to extract structural relational triple or
event from unstructured texts, often suffers from data scarcity issues. With
the development of pre-trained language models, many prompt-based approaches to
data-efficient information extraction have been proposed and achieved
impressive performance. However, existing prompt learning methods for
information extraction are still susceptible to several potential limitations:
(i) semantic gap between natural language and output structure knowledge with
pre-defined schema; (ii) representation learning with locally individual
instances limits the performance given the insufficient features. In this
paper, we propose a novel approach of schema-aware Reference As Prompt (RAP),
which dynamically leverage schema and knowledge inherited from global
(few-shot) training data for each sample. Specifically, we propose a
schema-aware reference store, which unifies symbolic schema and relevant
textual instances. Then, we employ a dynamic reference integration module to
retrieve pertinent knowledge from the datastore as prompts during training and
inference. Experimental results demonstrate that RAP can be plugged into
various existing models and outperforms baselines in low-resource settings on
four datasets of relational triple extraction and event extraction. In
addition, we provide comprehensive empirical ablations and case analysis
regarding different types and scales of knowledge in order to better understand
the mechanisms of RAP. Code is available in https://github.com/zjunlp/RAP.Comment: Work in progres