  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.
