  This paper describes a modular connectionist model of the acquisition of
receptive inflectional morphology. The model takes inputs in the form of phones
one at a time and outputs the associated roots and inflections. In its simplest
version, the network consists of separate simple recurrent subnetworks for root
and inflection identification; both networks take the phone sequence as inputs.
It is shown that the performance of the two separate modular networks is
superior to a single network responsible for both root and inflection
identification. In a more elaborate version of the model, the network learns to
use separate hidden-layer modules to solve the separate tasks of root and
inflection identification.
