Multi-task learning with Convolutional Neural Network (CNN) has shown great success in many Natural Language Processing (NLP) tasks. This success can be largely attributed to the feature sharing by fusing some layers among tasks. However, most existing approaches just fully or proportionally share the features without distinguishing the helpfulness of them. By that the network would be confused by the helpless even harmful features, generating undesired interference between tasks. In this paper, we introduce gate mechanism into multi-task CNN and propose a new Gated Sharing Unit, which can filter the feature flows between tasks and greatly reduce the interference. Experiments on 9 text classification datasets shows that our approach can learn selection rules automatically and gain a great improvement over strong baselines.
To address the problem of automatic shared parameter selection, Ruder et al. (2017a) automatically learned the latent multi-task sharing architecture, and REF used a gate mechanism that filters the feature flows between tasks.
Gated Multi-Task Network for Text Classification
NAACL-HLT
Computer Science