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Improving Recurrent Neural Networks with Predictive Propagation for Sequence Labelling

  • Son N. Tran
  • Qing Zhang
  • Anthony Nguyen
  • Xuan-Son Vu
  • Son Ngo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)

Abstract

Recurrent neural networks (RNNs) is a useful tool for sequence labelling tasks in natural language processing. Although in practice RNNs suffer a problem of vanishing/exploding gradient, their compactness still offers efficiency and make them less prone to overfitting. In this paper we show that by propagating the prediction of previous labels we can improve the performance of RNNs while keeping the number of parameters in RNNs unchanged and adding only one more step for inference. As a result, the models are still more compact and efficient than other models with complex memory gates. In the experiment, we evaluate the idea on optical character recognition and Chunking which achieve promising results.

Keywords

Natural language processing Recurrent neural networks Sequence labelling 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Son N. Tran
    • 1
  • Qing Zhang
    • 1
  • Anthony Nguyen
    • 1
  • Xuan-Son Vu
    • 2
  • Son Ngo
    • 3
  1. 1.The Australian E-Health Research CentreCSIROBrisbaneAustralia
  2. 2.Department of Computing ScienceUmeå UniversityUmeåSweden
  3. 3.Department of Computer ScienceFPT UniversityHanoiVietnam

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