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

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 a(n)b(n)c(n).