  Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not
perform well for small training sets. We address paradigm completion, the
morphological task of, given a partial paradigm, generating all missing forms.
We propose two new methods for the minimal-resource setting: (i) Paradigm
transduction: Since we assume only few paradigms available for training, neural
seq2seq models are able to capture relationships between paradigm cells, but
are tied to the idiosyncracies of the training set. Paradigm transduction
mitigates this problem by exploiting the input subset of inflected forms at
test time. (ii) Source selection with high precision (SHIP): Multi-source
models which learn to automatically select one or multiple sources to predict a
target inflection do not perform well in the minimal-resource setting. SHIP is
an alternative to identify a reliable source if training data is limited. On a
52-language benchmark dataset, we outperform the previous state of the art by
up to 9.71% absolute accuracy.
