On Evaluating the Generalization of LSTM Models in Formal Languages

@article{Suzgun2018OnET,
  title={On Evaluating the Generalization of LSTM Models in Formal Languages},
  author={Mirac Suzgun and Yonatan Belinkov and Stuart M. Shieber},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.01001}
}
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 $a^nb^n$, $a^nb^nc^n$, and $a^nb^nc^nd^n$. We investigate the influence of… CONTINUE READING
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Analysis Methods in Neural Language Processing: A Survey

  • Transactions of the Association for Computational Linguistics
  • 2018
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