ALANNO: An Active Learning Annotation System for Mortals

Josip Jukić, Fran Jelenić, Miroslav Bićanić, Jan Snajder


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.
Anthology ID:
2023.eacl-demo.26
Volume:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Danilo Croce, Luca Soldaini
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
228–235
Language:
URL:
https://aclanthology.org/2023.eacl-demo.26
DOI:
10.18653/v1/2023.eacl-demo.26
Bibkey:
Cite (ACL):
Josip Jukić, Fran Jelenić, Miroslav Bićanić, and Jan Snajder. 2023. ALANNO: An Active Learning Annotation System for Mortals. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 228–235, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
ALANNO: An Active Learning Annotation System for Mortals (Jukić et al., EACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.eacl-demo.26.pdf
Video:
 https://aclanthology.org/2023.eacl-demo.26.mp4