@inproceedings{cerezo-etal-2021-tools,
title = "Tools Impact on the Quality of Annotations for Chat Untangling",
author = "Cerezo, Jhonny and
Bravo-Marquez, Felipe and
Bergel, Alexandre Henri",
editor = "Kabbara, Jad and
Lin, Haitao and
Paullada, Amandalynne and
Vamvas, Jannis",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-srw.22",
doi = "10.18653/v1/2021.acl-srw.22",
pages = "215--220",
abstract = "The quality of the annotated data directly influences in the success of supervised NLP models. However, creating annotated datasets is often time-consuming and expensive. Although the annotation tool takes an important role, we know little about how it influences annotation quality. We compare the quality of annotations for the task of chat-untangling made by non-experts annotators using two different tools. The first is SLATE, an existing command-line based tool, and the second is Parlay, a new tool we developed that integrates mouse interaction and visual links. Our experimental results indicate that, while both tools perform similarly in terms of annotation quality, Parlay offers a significantly better user experience.",
}
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<abstract>The quality of the annotated data directly influences in the success of supervised NLP models. However, creating annotated datasets is often time-consuming and expensive. Although the annotation tool takes an important role, we know little about how it influences annotation quality. We compare the quality of annotations for the task of chat-untangling made by non-experts annotators using two different tools. The first is SLATE, an existing command-line based tool, and the second is Parlay, a new tool we developed that integrates mouse interaction and visual links. Our experimental results indicate that, while both tools perform similarly in terms of annotation quality, Parlay offers a significantly better user experience.</abstract>
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%0 Conference Proceedings
%T Tools Impact on the Quality of Annotations for Chat Untangling
%A Cerezo, Jhonny
%A Bravo-Marquez, Felipe
%A Bergel, Alexandre Henri
%Y Kabbara, Jad
%Y Lin, Haitao
%Y Paullada, Amandalynne
%Y Vamvas, Jannis
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F cerezo-etal-2021-tools
%X The quality of the annotated data directly influences in the success of supervised NLP models. However, creating annotated datasets is often time-consuming and expensive. Although the annotation tool takes an important role, we know little about how it influences annotation quality. We compare the quality of annotations for the task of chat-untangling made by non-experts annotators using two different tools. The first is SLATE, an existing command-line based tool, and the second is Parlay, a new tool we developed that integrates mouse interaction and visual links. Our experimental results indicate that, while both tools perform similarly in terms of annotation quality, Parlay offers a significantly better user experience.
%R 10.18653/v1/2021.acl-srw.22
%U https://aclanthology.org/2021.acl-srw.22
%U https://doi.org/10.18653/v1/2021.acl-srw.22
%P 215-220
Markdown (Informal)
[Tools Impact on the Quality of Annotations for Chat Untangling](https://aclanthology.org/2021.acl-srw.22) (Cerezo et al., ACL-IJCNLP 2021)
ACL
- Jhonny Cerezo, Felipe Bravo-Marquez, and Alexandre Henri Bergel. 2021. Tools Impact on the Quality of Annotations for Chat Untangling. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop, pages 215–220, Online. Association for Computational Linguistics.