Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification
Ran Wang, Xiao Hong Su, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 metaknowledge 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 suboptimal for LMTC tasks due to long-tailed label distribution and coexisting of few-and zeroshot 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 metalearning phase.Extensive experiments show that META-LMTC achieves state-of-the-art performance against strong baselines and can still enhance powerful BERTlike models.