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    <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.</abstract>
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%0 Conference Proceedings
%T Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification
%A Wang, Ran
%A Su, Xi’ao
%A Long, Siyu
%A Dai, Xinyu
%A Huang, Shujian
%A Chen, Jiajun
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F wang-etal-2021-meta-lmtc
%X 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.
%R 10.18653/v1/2021.emnlp-main.679
%U https://aclanthology.org/2021.emnlp-main.679
%U https://doi.org/10.18653/v1/2021.emnlp-main.679
%P 8633-8646

Markdown (Informal)

[Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification](https://aclanthology.org/2021.emnlp-main.679) (Wang et al., EMNLP 2021)

• Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification (Wang et al., EMNLP 2021)

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.