# Multi-Task Label Embedding for Text Classification

arXiv: https://arxiv.org/abs/1710.07210 · HTML: https://mlsanity.com/paper/1710.07210/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2017.

## Built on by (most cited first, 9 of 9)

- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Label Anchored Contrastive Learning for Language Understanding](https://mlsanity.com/paper/2205.10227/) (2022)
- [Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records](https://mlsanity.com/paper/2201.06779/) (2022)
- [SemSup: Semantic Supervision for Simple and Scalable Zero-shot Generalization](https://mlsanity.com/paper/2202.13100/) (2022)
- [Description-Enhanced Label Embedding Contrastive Learning for Text Classification](https://mlsanity.com/paper/2306.08817/) (2023)
- [Semantic Pivoting Model for Effective Event Detection](https://mlsanity.com/paper/2211.00709/) (2022)
- [Automatic Alignment of Discourse Relations of Different Discourse Annotation Frameworks](https://mlsanity.com/paper/2403.20196/) (2024)
- [Causal Invariance and Counterfactual Learning Driven Cooperative Game for Multi-Label Classification](https://mlsanity.com/paper/2512.00812/) (2025)

## Abstract (arXiv)

Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. We implement unsupervised, supervised and semi-supervised models of Multi-Task Label Embedding, all utilizing semantic correlations among tasks and making it particularly convenient to scale and transfer as more tasks are involved. Extensive experiments on five benchmark datasets for text classification show that our models can effectively improve performances of related tasks with semantic representations of labels and additional information from each other.