A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning

A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning

Honglun Zhang, Liqiang Xiao, Yongkun Wang, Yaohui Jin

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3385-3391. https://doi.org/10.24963/ijcai.2017/473

Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. However, most previous works only consider simple or weak interactions, thereby failing to model complex correlations among three or more tasks. In this paper, we propose a multi-task learning architecture with four types of recurrent neural layers to fuse information across multiple related tasks. The architecture is structurally flexible and considers various interactions among tasks, which can be regarded as a generalized case of many previous works. Extensive experiments on five benchmark datasets for text classification show that our model can significantly improve performances of related tasks with additional information from others.
Keywords:
Machine Learning: Transfer, Adaptation, Multi-task Learning
Machine Learning: Deep Learning
Natural Language Processing: Text Classification