@inproceedings{kawamoto-etal-2021-fast,
    title = "{FAST}: {F}ast {A}nnotation tool for {S}mar{T} devices",
    author = "Kawamoto, Shunyo  and
      Sawai, Yu  and
      Wakimoto, Kohei  and
      Zhang, Peinan",
    editor = "Adel, Heike  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.emnlp-demo.41/",
    doi = "10.18653/v1/2021.emnlp-demo.41",
    pages = "372--381",
    abstract = "Working with a wide range of annotators with the same attributes is crucial, as in real-world applications. Although such application cases often use crowd-sourcing mechanisms to gather a variety of annotators, most real-world users use mobile devices. In this paper, we propose {\textquotedblleft}FAST,{\textquotedblright} an annotation tool for application tasks that focuses on the user experience of mobile devices, which has not yet been focused on thus far. We designed FAST as a web application for use on any device with a flexible interface that can be customized to fit various tasks. In our experiments, we conducted crowd-sourced annotation for a sentiment analysis task with several annotators and evaluated annotation metrics such as speed, quality, and ease of use from the tool`s logs and user surveys. Based on the results of our experiments, we conclude that our system can annotate faster than existing methods while maintaining the annotation quality."
}