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
Link To Document