  As children acquire the knowledge of their language's morphology, they
invariably discover the productive processes that can generalize to new words.
Morphological learning is made challenging by the fact that even fully
productive rules have exceptions, as in the well-known case of English past
tense verbs, which features the -ed rule against the irregular verbs. The
Tolerance Principle is a recent proposal that provides a precise threshold of
exceptions that a productive rule can withstand. Its empirical application so
far, however, requires the researcher to fully specify rules defined over a set
of words. We propose a greedy search model that automatically hypothesizes
rules and evaluates their productivity over a vocabulary. When the search for
broader productivity fails, the model recursively subdivides the vocabulary and
continues the search for productivity over narrower rules. Trained on
psychologically realistic data from child-directed input, our model displays
developmental patterns observed in child morphology acquisition, including the
notoriously complex case of German noun pluralization. It also produces
responses to nonce words that, despite receiving only a fraction of the
training data, are more similar to those of human subjects than current neural
network models' responses are.
