Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Yi Huang, Buse Giledereli, Abdullatif Köksal, Arzucan Özgür, Elif Ozkirimli
Abstract
Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Resampling and re-weighting are common approaches used for addressing the class imbalance problem, however, they are not effective when there is label dependency besides class imbalance because they result in oversampling of common labels. Here, we introduce the application of balancing loss functions for multi-label text classification. We perform experiments on a general domain dataset with 90 labels (Reuters-21578) and a domain-specific dataset from PubMed with 18211 labels. We find that a distribution-balanced loss function, which inherently addresses both the class imbalance and label linkage problems, outperforms commonly used loss functions. Distribution balancing methods have been successfully used in the image recognition field. Here, we show their effectiveness in natural language processing. Source code is available at https://github.com/blessu/BalancedLossNLP.- Anthology ID:
- 2021.emnlp-main.643
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8153–8161
- Language:
- URL:
- https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.emnlp-main.643/
- DOI:
- 10.18653/v1/2021.emnlp-main.643
- Cite (ACL):
- Yi Huang, Buse Giledereli, Abdullatif Köksal, Arzucan Özgür, and Elif Ozkirimli. 2021. Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8153–8161, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution (Huang et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.emnlp-main.643.pdf
- Code
- Roche/BalancedLossNLP + additional community code
- Data
- Reuters-21578