  Neural models for the various flavours of morphological inflection tasks have
proven to be extremely accurate given ample labeled data -- data that may be
slow and costly to obtain. In this work we aim to overcome this annotation
bottleneck by bootstrapping labeled data from a seed as little as {\em five}
labeled paradigms, accompanied by a large bulk of unlabeled text. Our approach
exploits different kinds of regularities in morphological systems in a
two-phased setup, where word tagging based on {\em analogies} is followed by
word pairing based on {\em distances}. We experiment with the Paradigm Cell
Filling Problem over eight typologically different languages, and find that, in
languages with relatively simple morphology, orthographic regularities on their
own allow inflection models to achieve respectable accuracy. Combined
orthographic and semantic regularities alleviate difficulties with particularly
complex morpho-phonological systems. Our results suggest that hand-crafting
many tagged examples might be an unnecessary effort. However, more work is
needed in order to address rarely used forms.
