  Named entity recognition (NER) suffers from the scarcity of annotated
training data, especially for low-resource languages without labeled data.
Cross-lingual NER has been proposed to alleviate this issue by transferring
knowledge from high-resource languages to low-resource languages via aligned
cross-lingual representations or machine translation results. However, the
performance of cross-lingual NER methods is severely affected by the
unsatisfactory quality of translation or label projection. To address these
problems, we propose a Cross-lingual Entity Projection framework (CROP) to
enable zero-shot cross-lingual NER with the help of a multilingual labeled
sequence translation model. Specifically, the target sequence is first
translated into the source language and then tagged by a source NER model. We
further adopt a labeled sequence translation model to project the tagged
sequence back to the target language and label the target raw sentence.
Ultimately, the whole pipeline is integrated into an end-to-end model by the
way of self-training. Experimental results on two benchmarks demonstrate that
our method substantially outperforms the previous strong baseline by a large
margin of +3~7 F1 scores and achieves state-of-the-art performance.
