  Learning the past tense of English verbs - a seemingly minor aspect of
language acquisition - has generated heated debates since 1986, and has become
a landmark task for testing the adequacy of cognitive modeling. Several
artificial neural networks (ANNs) have been implemented, and a challenge for
better symbolic models has been posed. In this paper, we present a
general-purpose Symbolic Pattern Associator (SPA) based upon the decision-tree
learning algorithm ID3. We conduct extensive head-to-head comparisons on the
generalization ability between ANN models and the SPA under different
representations. We conclude that the SPA generalizes the past tense of unseen
verbs better than ANN models by a wide margin, and we offer insights as to why
this should be the case. We also discuss a new default strategy for
decision-tree learning algorithms.
