LSTM Recurrent Networks Learn Simple Context-Free and Context-Sensitive Languages  

Oleh:

Gers, F. A. ; Schmidhuber, E.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 6 (2001), page 1333-1340.
Topik: networks; LSTM; networks; simple context - free; context - sensitive languages

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Previous work on learning regular languages from exemplary training sequences showed that long short-term memory (LSTM) outperforms traditional recurrent neural networks (RNN s). We demonstrate LSTMs superior performance on context - free language benchmarks for RNN s, 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 RNN s to learn a simple context - sensitive language, namely a(n)b(n)c(n).

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