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
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 228–235
- Language:
- URL:
- https://aclanthology.org/2023.eacl-demo.26
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.eacl-demo.26.pdf