Discrete recurrent neural networks for grammatical inference

By Zheng Zeng, Rodney M. Goodman, Padhraic Smyth. 1994

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In Natural Language Processing

Citations in the corpus

Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets (2015, methodology): “Our work can be seen as a follow up of the research done in early nineties, when similar types of stack RNNs were studied (Pollack, 1991; Das et al., 1992; Mozer & Das, 1993; Zeng et al., 1994).”
Neural Programmer: Inducing Latent Programs with Gradient Descent (2015, background): “There has been some early work in using neural networks for learning context free grammar (Das et al., 1992a;b; Zeng et al., 1994) and context sensitive grammar (Steijvers, 1996; Gers & Schmidhuber, 2001) for small problems.”
Deep learning in neural networks: An overview (2014, methodology): “LSTM clearly outperformed previous RNNs on tasks that require learning the rules of regular languages describable by deterministic Finite State Automata (FSAs) (Watrous and Kuhn, 1992; Casey, 1996; Siegelmann, 1992; Blair and Pollack, 1997; Kalinke and Lehmann, 1998; Zeng et al., 1994; Manolios and ”
Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference (2001, background): “It is then possible to take advantage of the known capabilities of recurrent neural networks to learn grammars [22, 63] in order to capture any predictability in the evolution of the series.”
Incremental training of first order recurrent neural networks to predict a context-sensitive language (2003, background): “, 1993; Elman, 1993) or appeared to impose some restrictions on the way networks developed during the learning process which finally inhibited successful learning (Rohde & Plaut, 1997, 1999, 2003; Zeng et al., 1994).”
LSTM recurrent networks learn simple context-free and context-sensitive languages (2001, background): “RNN algorithms on numerous tasks involving real-valued or discrete inputs and targets [5], [7], including tasks that require to learn the rules of regular languages (RLs) describable by deterministic finite-state automata (DFA) [1], [2], [8], [21], [ 31 ].”
Context-free and context-sensitive dynamics in recurrent neural networks (2000, methodology): “RNNs have been handcrafted, sometimes using external stacks, to handle context-free (Das et al., 1993; Zeng et al., 1994; H¨olldobler et al., 1997; Tabor, 2000) and context-sensitive languages (Steijvers and Gr¨unwald, 1996).”