Proceedings

Recurrent Neural Network for Text Classification with Multi-Task Learning

Pengfei Liu, Xipeng Qiu, Xuanjing Huang

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2873-2878.

Abstract

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 multi-task 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.

Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Recurrent Neural Network for Text Classification with Multi-Task Learning. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 2873–2878. https://ijcai.org/proceedings/2016/408
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