  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 \url{http://nlp.fudan.edu.cn/data/}
