  Nen verbal morphology is remarkably complex; a transitive verb can take up to
1,740 unique forms. The combined effect of having a large combinatoric space
and a low-resource setting amplifies the need for NLP tools. Nen morphology
utilises distributed exponence - a non-trivial means of mapping form to
meaning. In this paper, we attempt to model Nen verbal morphology using
state-of-the-art machine learning models for morphological reinflection. We
explore and categorise the types of errors these systems generate. Our results
show sensitivity to training data composition; different distributions of verb
type yield different accuracies (patterning with E-complexity). We also
demonstrate the types of patterns that can be inferred from the training data
through the case study of syncretism.
