We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for
the general task of labeling structured data with textual descriptions. The
tool is implemented as an interactive application that reduces human efforts in
annotating large quantities of structured data, e.g. in the format of a table
or tree structure. By using a backend sequence-to-sequence model, our system
iteratively analyzes the annotated labels in order to better sample unlabeled
data. In a simulation experiment performed on annotating large quantities of
structured data, DART has been shown to reduce the total number of annotations
needed with active learning and automatically suggesting relevant labels.

介绍了一种轻量级的注释工具 ——Data AnnotatoR Tool (DART)，用于标记分层结构数据。通过使用后端序列到序列模型，该系统可以迭代地分析注释标签以更好地对未标记数据进行采样。在模拟实验中，DART 显示了其在标记大量结构化数据、减少注释总数以及自动建议相关标签等方面的优越性能。