  Neural methods have been shown to achieve high performance in Named Entity
Recognition (NER), but rely on costly high-quality labeled data for training,
which is not always available across languages. While previous works have shown
that unlabeled data in a target language can be used to improve cross-lingual
model performance, we propose a novel adversarial approach (AdvPicker) to
better leverage such data and further improve results. We design an adversarial
learning framework in which an encoder learns entity domain knowledge from
labeled source-language data and better shared features are captured via
adversarial training - where a discriminator selects less language-dependent
target-language data via similarity to the source language. Experimental
results on standard benchmark datasets well demonstrate that the proposed
method benefits strongly from this data selection process and outperforms
existing state-of-the-art methods; without requiring any additional external
resources (e.g., gazetteers or via machine translation). The code is available
at https://aka.ms/AdvPicker
