Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification
Ran Wang, Xi’ao Su, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen
Abstract
Large-scale multi-label text classification (LMTC) tasks often face long-tailed label distributions, where many labels have few or even no training instances. Although current methods can exploit prior knowledge to handle these few/zero-shot labels, they neglect the meta-knowledge contained in the dataset that can guide models to learn with few samples. In this paper, for the first time, this problem is addressed from a meta-learning perspective. However, the simple extension of meta-learning approaches to multi-label classification is sub-optimal for LMTC tasks due to long-tailed label distribution and coexisting of few- and zero-shot scenarios. We propose a meta-learning approach named META-LMTC. Specifically, it constructs more faithful and more diverse tasks according to well-designed sampling strategies and directly incorporates the objective of adapting to new low-resource tasks into the meta-learning phase. Extensive experiments show that META-LMTC achieves state-of-the-art performance against strong baselines and can still enhance powerful BERTlike models.- Anthology ID:
- 2021.emnlp-main.679
- 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:
- 8633–8646
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.679
- DOI:
- 10.18653/v1/2021.emnlp-main.679
- Cite (ACL):
- Ran Wang, Xi’ao Su, Siyu Long, Xinyu Dai, Shujian Huang, and Jiajun Chen. 2021. Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8633–8646, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification (Wang et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.emnlp-main.679.pdf
- Data
- EURLEX57K