A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing
Sophie Henning, William Beluch, Alexander Fraser, Annemarie Friedrich
Abstract
Many natural language processing (NLP) tasks are naturally imbalanced, as some target categories occur much more frequently than others in the real world. In such scenarios, current NLP models tend to perform poorly on less frequent classes. Addressing class imbalance in NLP is an active research topic, yet, finding a good approach for a particular task and imbalance scenario is difficult. In this survey, the first overview on class imbalance in deep-learning based NLP, we first discuss various types of controlled and real-world class imbalance. Our survey then covers approaches that have been explicitly proposed for class-imbalanced NLP tasks or, originating in the computer vision community, have been evaluated on them. We organize the methods by whether they are based on sampling, data augmentation, choice of loss function, staged learning, or model design. Finally, we discuss open problems and how to move forward.- Anthology ID:
- 2023.eacl-main.38
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 523–540
- Language:
- URL:
- https://aclanthology.org/2023.eacl-main.38
- DOI:
- 10.18653/v1/2023.eacl-main.38
- Cite (ACL):
- Sophie Henning, William Beluch, Alexander Fraser, and Annemarie Friedrich. 2023. A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 523–540, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing (Henning et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.eacl-main.38.pdf