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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.emnlp-main.414.pdf