Despite its successes, Rumelhart and McClelland's (1986) well-known ap(cid:173) proach to the learning of morphophonemic rules suffers from two deficien(cid:173) cies: (1) It performs the artificial task of associating forms with forms rather than perception or production. This paper describes a model which addresses both objections. Using a simple recurrent architecture which takes both forms and "meanings" as inputs, the model learns to generate verbs in one or another "tense", given arbitrary meanings, and to recognize the tenses of verbs. Furthermore, it fails to learn reversal processes unknown in human language.

In the debate over the power of connectionist models to handle linguistic phenomena, considerable attention has been focused on the learning of simple morphological rules. It is a straightforward matter in a symbolic system to specify how the meanings of a stem and a bound morpheme combine to yield the meaning of a whole word and how the form of the bound morpheme depends on the shape of the stem. In a distributed connectionist system, however, where there may be no explicit morphemes, words, or rules, things are not so simple. The most important work in this area has been that of Rumelhart and McClelland (1986), together with later extensions by Marchman and Plunkett (1989). The networks involved were trained to associate English verb stems with the corresponding past-tense forms, successfully generating both regular and irregular forms and generalizing to novel inputs.

In the debate over the power of connectionist models to handle linguistic phenomena, considerable attention has been focused on the learning of simple morphological rules. It is a straightforward matter in a symbolic system to specify how the meanings of a stem and a bound morpheme combine to yield the meaning of a whole word and how the form of the bound morpheme depends on the shape of the stem. In a distributed connectionist system, however, where there may be no explicit morphemes, words, or rules, things are not so simple. The most important work in this area has been that of Rumelhart and McClelland (1986), together with later extensions by Marchman and Plunkett (1989). The networks involved were trained to associate English verb stems with the corresponding past-tense forms, successfully generating both regular and irregular forms and generalizing to novel inputs.

In the debate over the power of connectionist models to handle linguistic phenomena, considerableattention has been focused on the learning of simple morphological rules. It is a straightforward matter in a symbolic system to specify how the meanings ofa stem and a bound morpheme combine to yield the meaning of a whole word and how the form of the bound morpheme depends on the shape of the stem. In a distributed connectionist system, however, where there may be no explicit morphemes, words, or rules, things are not so simple. The most important work in this area has been that of Rumelhart and McClelland (1986), together with later extensions by Marchman and Plunkett (1989). The networks involvedwere trained to associate English verb stems with the corresponding past-tense forms, successfully generating both regular and irregular forms and generalizing tonovel inputs. This work established that rule-like linguistic behavior 605 606 Gasser and Lee could be achieved in a system with no explicit rules. However, it did have important limitations, among them the following: 1. The representation of linguistic form was inadequate. This is clear, for example, fromthe fact that distinct lexical items may be associated with identical representations (Pinker & Prince, 1988).