  Many languages' inflectional morphological systems are replete with
irregulars, i.e., words that do not seem to follow standard inflectional rules.
In this work, we quantitatively investigate the conditions under which
irregulars can survive in a language over the course of time. Using recurrent
neural networks to simulate language learners, we test the diachronic relation
between frequency of words and their irregularity.
