@inproceedings{jukic-etal-2023-alanno,
    title = "{ALANNO}: An Active Learning Annotation System for Mortals",
    author = "Juki{\'c}, Josip  and
      Jeleni{\'c}, Fran  and
      Bi{\'c}ani{\'c}, Miroslav  and
      \v{S}najder, Jan",
    editor = "Croce, Danilo  and
      Soldaini, Luca",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-demo.26",
    doi = "10.18653/v1/2023.eacl-demo.26",
    pages = "228--235",
    abstract = "Supervised machine learning has become the cornerstone of today{'}s data-driven society, increasing the need for labeled data. However, the process of acquiring labels is often expensive and tedious. One possible remedy is to use active learning (AL) {--} a special family of machine learning algorithms designed to reduce labeling costs. Although AL has been successful in practice, a number of practical challenges hinder its effectiveness and are often overlooked in existing AL annotation tools. To address these challenges, we developed ALANNO, an open-source annotation system for NLP tasks equipped with features to make AL effective in real-world annotation projects. ALANNO facilitates annotation management in a multi-annotator setup and supports a variety of AL methods and underlying models, which are easily configurable and extensible.",
}