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Seq2Seq based neural architectures have become the go-to architecture to apply to sequence to sequence language tasks.
Aspects of the Theory of Syntax
Noam Chomsky. 1965 · 1965
Earlier work this paper cites.
A theory of the learnable
L. G. Valiant. 1984 · 1984
Earlier work this paper cites.
Distributed representations, simple recurrent networks, and grammatical structure
Jeffrey L Elman. 1991 · 1991
Earlier work this paper cites.
Learning to count without a counter: A case study of dynamics and activation landscapes in recurrent networks
Janet Wiles and Jeff Elman. 1995 · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Recurrent neural networks can learn to implement symbol-sensitive counting
Paul Rodriguez and Janet Wiles. 1998 · 1998
Earlier work this paper cites.
Lstm recurrent networks learn simple context-free and context-sensitive languages
F. A. Gers and E. Schmidhuber. 2001 · 2001
Earlier work this paper cites.
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