Neuralnetworkbasedmethodshaveobtainedgreat progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multitasklearningframeworktojointlylearnacrossmultiple related tasks. Based on recurrent neural network, we propose three different mechanisms of sharinginformationtomodeltextwithtask-specific and shared layers. The entire network is trained jointly on al l these tasks. Experiments on four benchmark text classification tasks show that our proposedmodelscanimprovetheperformanceofa task with the help of other related tasks.