When Irregularity Helps: A Subclass Analysis of Inductive Bias in Neural Morphology

ArXi:2605.20558v1 Announce Type: new Neural morphological generation systems often achieve high aggregate accuracy on benchmark datasets, yet such performance can conceal systematic errors concentrated in rare morphological subclasses. We examine Japanese past-tense verb inflection and show that a very small, structurally specific irregular subtype (<1% of data) accounts for a disproportionate share of model errors.