LSTM Recurrent Networks Learn Simple Context-Free and Context-Sensitive Languages
Abstract
Previous work on learning regular languages from exemplary training sequences showed that long short-term memory (LSTM) outperforms traditional recurrent neural networks (RNNs). Here we demonstrate LSTMs superior performance on context-free language (CFL) 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 (CSL), namely
- Publication:
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IEEE Transactions on Neural Networks
- Pub Date:
- 2001
- DOI:
- Bibcode:
- 2001ITNN...12.1333G
- Keywords:
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- Context-free languages (CFLs);
- context-sensitive languages (CSLs);
- long short-term memory (LSTM);
- recurrent neural networks (RNNs)