The performance of current supervised AI systems is tightly connected to the
availability of annotated datasets. Annotations are usually collected through
annotation tools, which are often designed for specific tasks and are difficult
to customize. Moreover, existing annotation tools with an active learning
mechanism often only support limited use cases. To address these limitations,
we present EASE, an Easily-Customized Annotation System Powered by Efficiency
Enhancement Mechanisms. \sysname provides modular annotation units for building
customized annotation interfaces and also provides multiple back-end options
that suggest annotations using (1) multi-task active learning; (2) demographic
feature based active learning; (3) a prompt system that can query the API of
large language models. We conduct multiple experiments and user studies to
evaluate our system's flexibility and effectiveness. Our results show that our
system can meet the diverse needs of NLP researchers and significantly
accelerate the annotation process.

提供一款可定制的注释系统 ——EASE，使用多任务主动学习、基于人口统计特征的主动学习和查询大型语言模型的提示系统作为多个后端选项，可以满足自然语言处理研究人员的多样化需求，并显著加速注释过程。