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.
翻译:我们提出了一个轻量级说明工具,即数据AnnotatoR工具(DART),用于对结构化数据进行文字描述的标签,该工具作为一种互动应用,可以减少人类在批注大量结构化数据方面所做的努力,例如用表格或树结构的形式。通过使用后端序列到序列模型,我们的系统对附加说明的标签进行迭代分析,以便更好地抽样无标签数据。在对大量结构化数据进行批注的模拟实验中,DART被显示为通过积极学习和自动建议相关标签,减少了所需的说明总数。