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.
@article{arxiv.2211.06224,
title = {ALANNO: An Active Learning Annotation System for Mortals},
author = {Josip Jukić and Fran Jelenić and Miroslav Bićanić and Jan Šnajder},
journal= {arXiv preprint arXiv:2211.06224},
year = {2023}
}