  For languages with no annotated resources, transferring knowledge from
rich-resource languages is an effective solution for named entity recognition
(NER). While all existing methods directly transfer from source-learned model
to a target language, in this paper, we propose to fine-tune the learned model
with a few similar examples given a test case, which could benefit the
prediction by leveraging the structural and semantic information conveyed in
such similar examples. To this end, we present a meta-learning algorithm to
find a good model parameter initialization that could fast adapt to the given
test case and propose to construct multiple pseudo-NER tasks for meta-training
by computing sentence similarities. To further improve the model's
generalization ability across different languages, we introduce a masking
scheme and augment the loss function with an additional maximum term during
meta-training. We conduct extensive experiments on cross-lingual named entity
recognition with minimal resources over five target languages. The results show
that our approach significantly outperforms existing state-of-the-art methods
across the board.
