• DocumentCode
    1547667
  • Title

    LSTM recurrent networks learn simple context-free and context-sensitive languages

  • Author

    Gers, Felix A. ; Schmidhuber, Jürgen

  • Author_Institution
    IDSIA, Manno, Switzerland
  • Volume
    12
  • Issue
    6
  • fYear
    2001
  • fDate
    11/1/2001 12:00:00 AM
  • Firstpage
    1333
  • Lastpage
    1340
  • Abstract
    Previous work on learning regular languages from exemplary training sequences showed that long short-term memory (LSTM) outperforms traditional recurrent neural networks (RNNs). We demonstrate LSTMs superior performance on context-free language benchmarks for RNNs, and show that it works even better than previous hardwired or highly specialized architectures. To the best of our knowledge, LSTM variants are also the first RNNs to learn a simple context-sensitive language, namely anbncn
  • Keywords
    context-free languages; context-sensitive languages; learning (artificial intelligence); recurrent neural nets; context-free language; context-sensitive language; long short-term memory; recurrent neural networks; regular languages; Backpropagation algorithms; Bridges; Computational complexity; Delay effects; Hidden Markov models; Learning automata; Neural networks; Recurrent neural networks; Resonance light scattering; State-space methods;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.963769
  • Filename
    963769