  Recently, neural methods have achieved state-of-the-art (SOTA) results in
Named Entity Recognition (NER) tasks for many languages without the need for
manually crafted features. However, these models still require manually
annotated training data, which is not available for many languages. In this
paper, we propose an unsupervised cross-lingual NER model that can transfer NER
knowledge from one language to another in a completely unsupervised way without
relying on any bilingual dictionary or parallel data. Our model achieves this
through word-level adversarial learning and augmented fine-tuning with
parameter sharing and feature augmentation. Experiments on five different
languages demonstrate the effectiveness of our approach, outperforming existing
models by a good margin and setting a new SOTA for each language pair.
