Neural network based methods have obtained great 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 multitask learning framework to jointly learn across multiple related tasks. Based on recurrent neural network, we propose three different mechanisms of sharing information to model text with task-specific and shared layers. The entire network is trained jointly on all these tasks. Experiments on four benchmark text classification tasks show that our proposed models can improve the performance of a task with the help of other related tasks.
Liu et al. REF , based on the same hierarchical principle, use the multi-task learning framework to improve the performance of their model in text classification and other related tasks.
Recurrent Neural Network for Text Classification with Multi-Task Learning
ArXiv
ArXiv
Computer Science