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

arXiv: https://arxiv.org/abs/2010.04141 · HTML: https://mlsanity.com/paper/2010.04141/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 12 of 12)

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- [Learning Fine-grained Fact-Article Correspondence in Legal Cases](https://mlsanity.com/paper/2104.10726/) (2021)
- [Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling](https://mlsanity.com/paper/2102.03551/) (2021)
- [Time-Aware Ancient Chinese Text Translation and Inference](https://mlsanity.com/paper/2107.03179/) (2021)
- [The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation](https://mlsanity.com/paper/2108.06614/) (2021)
- [LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training](https://mlsanity.com/paper/2112.01404/) (2021)
- [ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts](https://mlsanity.com/paper/2112.06724/) (2021)
- [EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms](https://mlsanity.com/paper/2305.14169/) (2023)
- [Revisiting Sample Size Determination in Natural Language Understanding](https://mlsanity.com/paper/2307.00374/) (2023)

## Abstract (arXiv)

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.