Default Generalization in Connectionist Networks

A potential problem for connectionist accounts of inflectional morphology is the need to learn a ‘default’ inflection (Prasada & Pinker, 1993). The early connectionist work of Rumelhart & McClelland (1986) might be interpreted as suggesting that a network can learn to treat a given inflection as the ‘elsewhere’ case only if it applies to a much larger class of items than any other inflection. This claim is true of the Rumelhart & McClelland (1986) model, which was a two-layer network subject to the computational limitations on networks of that class (Minsky & Papert, 1969). However, it does not generalize to current models, which have available more sophisticated architectures and learning algorithms. In the current paper we explain the basis of the distinction, and demonstrate that given more appropriate architectural assumptions, connectionist models are perfectly capable of learning a default category and generalizing as required, even in the absence of superior type frequency.