Using Simple Recurrent Networks to Learn Fixed-Length Representations of Variable-Length Strings

Christopher T. Kello, Daragh E. Sibley, and Andrew Colombi

Four connectionist models are reported that learn static representations of variable-length strings using a novel autosequencer architecture. These representations were learned as plans for a simple recurrent network to regenerate a given input sequence. Results showed that the autosequencer can be used to address the dispersion problem because the positions and identities of letters in a string were integrated over learning into the plan representations. Results also revealed a moderate degree of compositionality in the plan representations.

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