Abstract
Recent advancements in noisy multi-label text classification have primarily relied on the class-conditional noise (CCN) assumption, which treats each label independently undergoing label flipping to generate noisy labels. However, in real-world scenarios, noisy labels often exhibit dependencies with true labels. In this study, we validate through hypothesis testing that real-world datasets are unlikely to adhere to the CCN assumption, indicating that label noise is dependent on the labels. To address this, we introduce a label-specific denoising framework designed to counteract label-dependent noise. The framework initially presents a holistic selection metric that evaluates noisy labels by concurrently considering loss information, ranking information, and feature centroid. Subsequently, it identifies and corrects noisy labels individually for each label category in a fine-grained manner. Extensive experiments on benchmark datasets demonstrate the effectiveness of our method under both synthetic and real-world noise conditions, significantly improving performance over existing state-of-the-art models.- Anthology ID:
- 2024.findings-emnlp.324
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5674–5688
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.324/
- DOI:
- 10.18653/v1/2024.findings-emnlp.324
- Cite (ACL):
- Pengyu Xu, Liping Jing, and Jian Yu. 2024. Enhancing Multi-Label Text Classification under Label-Dependent Noise: A Label-Specific Denoising Framework. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5674–5688, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Multi-Label Text Classification under Label-Dependent Noise: A Label-Specific Denoising Framework (Xu et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.324.pdf