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

DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool