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Mind and Language, 9, 273-287, 1994.
Generalization and Connectionist Language Learning
Morten H. Christiansen & Nick Chater
Abstract
The performance of a learning system may be assessed by its ability to
generalize from past experience to novel stimuli. Recently, Hadley
(1994) has criticized connectionist language learning researchers for
not approaching generalization in a sophisticated manner. This paper
discusses Hadley's thesis concerning generalization and connectionist
language learning. Motivated by linguistic considerations, we provide
more formal and precise definitions of three varities of
generalization. Connectionist simulations are presented using simple
recurrent networks. The simulation results are evaluated in the light
of the revised definitions, and it is concluded that connectionist
models can, at least in part, accommodate sophisticated
generalization. We consider the prospects for connectionist and other
approaches to language learning for meeting these criteria in general.
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