A Survey of Active Learning for Natural Language Processing

Zhisong Zhang, Emma Strubell, Eduard Hovy


Abstract
In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions.
Anthology ID:
2022.emnlp-main.414
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6166–6190
Language:
URL:
https://aclanthology.org/2022.emnlp-main.414
DOI:
10.18653/v1/2022.emnlp-main.414
Bibkey:
Cite (ACL):
Zhisong Zhang, Emma Strubell, and Eduard Hovy. 2022. A Survey of Active Learning for Natural Language Processing. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6166–6190, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
A Survey of Active Learning for Natural Language Processing (Zhang et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-main.414.pdf