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

AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER