  We propose to cast the task of morphological inflection - mapping a lemma to
an indicated inflected form - for resource-poor languages as a meta-learning
problem. Treating each language as a separate task, we use data from
high-resource source languages to learn a set of model parameters that can
serve as a strong initialization point for fine-tuning on a resource-poor
target language. Experiments with two model architectures on 29 target
languages from 3 families show that our suggested approach outperforms all
baselines. In particular, it obtains a 31.7% higher absolute accuracy than a
previously proposed cross-lingual transfer model and outperforms the previous
state of the art by 1.7% absolute accuracy on average over languages.
