# Adversarial Multi-task Learning for Text Classification

arXiv: https://arxiv.org/abs/1704.05742 · HTML: https://mlsanity.com/paper/1704.05742/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2017.

## Built on by (most cited first, 30 of 91)

- [Is GPT-3 a Good Data Annotator?](https://mlsanity.com/paper/2212.10450/) (2022)
- [UniT: Multimodal Multitask Learning with a Unified Transformer](https://mlsanity.com/paper/2102.10772/) (2021)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs](https://mlsanity.com/paper/2206.04674/) (2022)
- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [Counter-Interference Adapter for Multilingual Machine Translation](https://mlsanity.com/paper/2104.08154/) (2021)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Dual-Balancing for Multi-Task Learning](https://mlsanity.com/paper/2308.12029/) (2023)
- [A Survey of Adversarial Defences and Robustness in NLP](https://mlsanity.com/paper/2203.06414/) (2022)
- [CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging](https://mlsanity.com/paper/2505.06977/) (2025)
- [Specializing Multilingual Language Models: An Empirical Study](https://mlsanity.com/paper/2106.09063/) (2021)
- [Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation](https://mlsanity.com/paper/2309.13700/) (2023)
- [Deep Co-Attention Network for Multi-View Subspace Learning](https://mlsanity.com/paper/2102.07751/) (2021)
- [Pose-disentangled Contrastive Learning for Self-supervised Facial Representation](https://mlsanity.com/paper/2211.13490/) (2022)
- [Conditional Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.10176/) (2021)
- [DiSparse: Disentangled Sparsification for Multitask Model Compression](https://mlsanity.com/paper/2206.04662/) (2022)
- [Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery](https://mlsanity.com/paper/2201.04437/) (2022)
- [Supervised Fine-tuning in turn Improves Visual Foundation Models](https://mlsanity.com/paper/2401.10222/) (2024)
- [Task Arithmetic in Trust Region: A Training-Free Model Merging Approach to Navigate Knowledge Conflicts](https://mlsanity.com/paper/2501.15065/) (2025)
- [ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation](https://mlsanity.com/paper/2203.03888/) (2022)
- [Evaluating the Values of Sources in Transfer Learning](https://mlsanity.com/paper/2104.12567/) (2021)
- [A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets](https://mlsanity.com/paper/2211.03533/) (2022)
- [GO4Align: Group Optimization for Multi-Task Alignment](https://mlsanity.com/paper/2404.06486/) (2024)
- [A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck](https://mlsanity.com/paper/2208.13017/) (2022)
- [Mixup Regularized Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.00467/) (2021)
- [Mere Contrastive Learning for Cross-Domain Sentiment Analysis](https://mlsanity.com/paper/2208.08678/) (2022)
- [Pay More Attention to Relation Exploration for Knowledge Base Question Answering](https://mlsanity.com/paper/2305.02118/) (2023)
- [Adversarial Multi-task Learning for End-to-end Metaphor Detection](https://mlsanity.com/paper/2305.16638/) (2023)
- [Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection](https://mlsanity.com/paper/2104.09261/) (2021)

## Abstract (arXiv)

Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks. In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other. We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach. Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks. The datasets of all 16 tasks are publicly available at urlhttp://nlp.fudan.edu.cn/data/