  Neural networks have long been at the center of a debate around the cognitive
mechanism by which humans process inflectional morphology. This debate has
gravitated into NLP by way of the question: Are neural networks a feasible
account for human behavior in morphological inflection? We address that
question by measuring the correlation between human judgments and neural
network probabilities for unknown word inflections. We test a larger range of
architectures than previously studied on two important tasks for the cognitive
processing debate: English past tense, and German number inflection. We find
evidence that the Transformer may be a better account of human behavior than
LSTMs on these datasets, and that LSTM features known to increase inflection
accuracy do not always result in more human-like behavior.
