Zero-Resource Cross-Lingual Named Entity Recognition
This addresses the challenge of NER for low-resource languages, enabling entity recognition without manual annotation, though it is incremental as it builds on existing neural methods.
The paper tackles the problem of Named Entity Recognition (NER) for languages lacking annotated training data by proposing an unsupervised cross-lingual model that transfers knowledge without bilingual resources, achieving state-of-the-art results across five languages with significant performance improvements.
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.