论文标题
DART：轻巧的质量挑战性数据对文本注释工具
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
论文作者
论文摘要
我们提出了一个轻巧的注释工具，即数据注释工具（DART），用于用文本描述标记结构化数据的一般任务。该工具被实现为一种交互应用，可减少人类在注释大量结构化数据（例如以表或树结构的格式。通过使用后端序列到序列模型，我们的系统迭代分析了带注释的标签，以便更好地采样未标记的数据。在对大量结构化数据进行注释的模拟实验中，DART已被证明可减少主动学习所需的注释总数，并自动提出相关标签。
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.
