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Recurrent Neural Network for Text Classification with ...
Recurrent Neural Network for TextClassification with Multi-Task LearningPengfei Liu Xipeng Qiu Xuanjing HuangShanghai Key Laboratory of Intelligent Information Processing, Fudan UniversitySchool of Computer Science, Fudan University825 Zhangheng Road, Shanghai, Network based methods have obtained greatprogress on a variety of natural language process-ing tasks. However, in most previous works, themodels are learned based on single-task super-vised objectives, which often suffer from insuffi-cient training data. In this paper, we use the multi-task learning framework to jointly learn across mul-tiple related tasks. Based on Recurrent Neural net-work, we propose three different mechanisms ofsharing information to model text with task-specificand shared layers. The entire Network is trainedjointly on all these tasks. Experiments on fourbenchmark text Classification tasks show that ourproposed models can improve the performance of atask with the help of other related IntroductionDistributed representations of words have been widely usedin many natural language processing (NLP) tasks.
Figure 2: Three architectures for modelling text with multi-task learning. Motivated by the success of multi-task learning [Caruana, 1997], we propose three multi-task models to leverage super-vised data from many related tasks. Deep neural model is well suited for multi-task learning since the features learned from a task may be useful for ...
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