ALANNO: An Active Learning Annotation System for Mortals

作     者：Jukić, Josip Jelenić, Fran Bićanić, Miroslav Šnajder, Jan 

作者机构：University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab Croatia 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Learning algorithms 

摘      要：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. © 2022, CC BY.