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.
CITATION STYLE
Jukić, J., Jelenić, F., Bićanić, M., & Šnajder, J. (2023). ALANNO: An Active Learning Annotation System for Mortals. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations (pp. 228–235). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.eacl-demo.26
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