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        <title>DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool</title>
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        <namePart type="given">Ernie</namePart>
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    <name type="personal">
        <namePart type="given">Xiaoyu</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-demos.3</identifier>
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%0 Conference Proceedings
%T DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
%A Chang, Ernie
%A Caplinger, Jeriah
%A Marin, Alex
%A Shen, Xiaoyu
%A Demberg, Vera
%Y Ptaszynski, Michal
%Y Ziolko, Bartosz
%S Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations
%D 2020
%8 December
%I International Committee on Computational Linguistics (ICCL)
%C Barcelona, Spain (Online)
%F chang-etal-2020-dart
%X 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.
%R 10.18653/v1/2020.coling-demos.3
%U https://aclanthology.org/2020.coling-demos.3
%U https://doi.org/10.18653/v1/2020.coling-demos.3
%P 12-17

Markdown (Informal)

[DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool](https://aclanthology.org/2020.coling-demos.3) (Chang et al., COLING 2020)

• DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool (Chang et al., COLING 2020)

ACL

• Ernie Chang, Jeriah Caplinger, Alex Marin, Xiaoyu Shen, and Vera Demberg. 2020. DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool. In Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations, pages 12–17, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).