# Does Head Label Help for Long-Tailed Multi-Label Text Classification

arXiv: https://arxiv.org/abs/2101.09704 · HTML: https://mlsanity.com/paper/2101.09704/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [What is the Effect of Importance Weighting in Deep Learning?](https://mlsanity.com/paper/1812.03372/) (2018)
- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](https://mlsanity.com/paper/1506.07285/) (2015)
- [Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling](https://mlsanity.com/paper/1611.06639/) (2016)
- [Large-scale Multi-label Text Classification - Revisiting Neural Networks](https://mlsanity.com/paper/1312.5419/) (2013)
- [Deep Extreme Multi-label Learning](https://mlsanity.com/paper/1704.03718/) (2017)
- [Deep Semantic Role Labeling with Self-Attention](https://mlsanity.com/paper/1712.01586/) (2017)
- [GILE: A Generalized Input-Label Embedding for Text Classification](https://mlsanity.com/paper/1806.06219/) (2018)
- [Explicit Interaction Model towards Text Classification](https://mlsanity.com/paper/1811.09386/) (2018)
- [A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)](https://mlsanity.com/paper/1805.07799/) (2018)
- [Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1905.10070/) (2019)
- [Interaction Matching for Long-Tail Multi-Label Classification](https://mlsanity.com/paper/2005.08805/) (2020)

## Built on by (most cited first, 13 of 13)

- [Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification](https://mlsanity.com/paper/2112.07928/) (2021)
- [Feature Fusion from Head to Tail for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2306.06963/) (2023)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Towards Heterogeneous Long-tailed Learning: Benchmarking, Metrics, and Toolbox](https://mlsanity.com/paper/2307.08235/) (2023)
- [Curiosity Meets Cooperation: A Game-Theoretic Approach to Long-Tail Multi-Label Learning](https://mlsanity.com/paper/2510.17520/) (2025)
- [LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification](https://mlsanity.com/paper/2103.14620/) (2021)
- [LabelCoRank: Revolutionizing Long Tail Multi-Label Classification with Co-Occurrence Reranking](https://mlsanity.com/paper/2503.07968/) (2025)
- [Label prompt for multi-label text classification](https://mlsanity.com/paper/2106.10076/) (2021)
- [Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification](https://mlsanity.com/paper/2211.10685/) (2022)
- [A Debiased Nearest Neighbors Framework for Multi-Label Text Classification](https://mlsanity.com/paper/2408.03202/) (2024)
- [PI-H2T: Enhancing Long-Tailed Visual Recognition with Permutation-Invariant and Head-to-Tail Feature Fusion](https://mlsanity.com/paper/2506.00625/) (2025)
- [Enhancing Rare Codes via Probability-Biased Directed Graph Attention for Long-Tail ICD Coding](https://mlsanity.com/paper/2511.09559/) (2025)
- [Mastering the Minority: An Uncertainty-guided Multi-Expert Framework for Challenging-tailed Sequence Learning](https://mlsanity.com/paper/2603.15708/) (2026)

## Abstract (arXiv)

Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label frequency often exhibits a long tail, i.e., a few labels are associated with a large number of documents (a.k.a. head labels), while a large fraction of labels are associated with a small number of documents (a.k.a. tail labels). To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels. The meta-knowledge is the mapping from few-shot network parameters to many-shot network parameters, which aims to promote the generalizability of tail classifiers. Extensive experimental results on three benchmark datasets demonstrate that HTTN consistently outperforms the state-of-the-art methods. The code and hyper-parameter settings are released for reproducibility