Corpus ID: 208162197

On Evaluating the Generalization of LSTM Models in Formal Languages

@inproceedings{Paulson2018OnET,
  title={On Evaluating the Generalization of LSTM Models in Formal Languages},
  author={John A. Paulson},
  year={2018}
}

John A. Paulson

Published 2018

Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a dominant model for language processing. Yet, there still remains an uncertainty regarding their language learning capabilities. In this paper, we empirically evaluate the inductive learning capabilities of Long Short-Term Memory networks, a popular extension of simple RNNs, to learn simple formal languages, in particular ab, abc, and abcd. We investigate the influence of various aspects of… CONTINUE READING

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