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.
The German plural system has become a focal point for conflicting theories of language, both linguistic and cognitive. We present simulation results with three simple classifiers - an ordinary nearest neighbour algorithm, Nosofsky's Generalized Context Model' (GCM) and a standard, three-layer backprop network - predicting the plural class from a phonological representation of the singular in German. Though these are absolutely minimal' models, in terms of architecture and input information, they nevertheless do remarkably well. The nearest neighbour predicts the correct plural class with an accuracy of 72 for a set of 24,640 nouns from the CELEX database. With a subset of 8,598 (non-compound) nouns, the nearest neighbour, the GCM and the network score 71.0 , 75.0 and 83.5 , respectively, on novel items. Furthermore, they outperform a hybrid, pattern-associator + default rule', model, as proposed by (1995), on this data set. The German plural system has recently become a focal point for conflicting theories of language, both linguistic and cognitive. (1995) highlight the German plural as support for the dual-route account of inflectional morphology first proposed by Pinker and colleagues (Pinker & Prince, 1988). On the dual-route account, inflectional morphology is universally subserved by a symbolic rule route which deals with regular inflection and an associative memory component which deals with irregular inflection. This contrasts with single-route connectionist systems. We seek to counter supposed evidence for the dual-route account through large-scale simulations as well as through experimental data. We argue that, in its current form, the dual-route account is incapable of generating experimental data provided by (1995) as support. Finally, we provide direct quantitative comparisons between single-route and dual-route models of German plural inflection and find single-route performance superior on these tests. 