Multi-task learning has an ability to share the knowledge among related tasks and implicitly increase the training data. However, it has long been frustrated by the interference among tasks. This paper investigates the performance of capsule network for text, and proposes a capsule-based multi-task learning architecture, which is unified, simple and effective. With the advantages of capsules for feature clustering, proposed task routing algorithm can cluster the features for each task in the network, which helps reduce the interference among tasks. Experiments on six text classification datasets demonstrate the effectiveness of our models and their characteristics for feature clustering.
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