Zero-Resource Cross-Lingual Named Entity Recognition

Understand

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.