Can advances in NLP help advance cognitive modeling? We examine the role of artificial neural networks, the current state of the art in many common NLP tasks, by returning to a classic case study. In 1986, Rumelhart and McClelland famously introduced a neural architecture that learned to transduce English verb stems to their past tense forms. Shortly thereafter, Pinker & Prince (1988) presented a comprehensive rebuttal of many of Rumelhart and McClelland's claims. Much of the force of their attack centered on the empirical inadequacy of the Rumelhart and McClelland (1986) model. Today, however, that model is severely outmoded. We show that the Encoder-Decoder network architectures used in modern NLP systems obviate most of Pinker and Prince's criticisms without requiring any simplication of the past tense mapping problem. We suggest that the empirical performance of modern networks warrants a re-examination of their utility in linguistic and cognitive modeling.
In this section, we first describe direct followups to the original 1986 model, employing various neural architectures. Then we review competing non-neural systems of context-sensitive rewrite rules in the style of the Sound Pattern of English (SPE) @cite_19 , as favored by Pinker and Prince.
@cite_19
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Since this classic work in phonology was published in 1968, there has been no other book that gives as broad a view of the subject, combining generally applicable theoretical contributions with analysis of the details of a single language. The theoretical issues raised in The Sound Pattern of English continue to be critical to current phonology, and in many instances the solutions proposed by Chomsky and Halle have yet to be improved upon.Noam Chomsky and Morris Halle are Institute Professors of Linguistics and Philosophy at MIT.