mlsanityData to 7 Oct 2026

Adversarial Multi-task Learning for Text Classification

arXiv 1704.05742 ↗First posted 19 Apr 2017cs.CLcited by 91

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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/

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2017.

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Citing papers per year: 2021: 24; 2022: 27; 2023: 15; 2024: 13; 2025: 11; 2026: 1.

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