DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
This tool addresses the challenge of efficiently annotating large structured datasets for data-to-text tasks, though it appears incremental as it builds on existing active learning methods.
The authors tackled the problem of labeling structured data with textual descriptions by developing DART, a lightweight annotation tool that uses active learning and label suggestions to reduce annotation effort, showing in simulations that it decreases the total number of annotations needed.
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.