Papers › Zero-Resource Cross-Lingual Named Entity Recognition
Zero-Resource Cross-Lingual Named Entity Recognition
M Saiful Bari, Shafiq Joty, Prathyusha Jwalapuram
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Low Resource Named Entity Recognition | CONLL 2003 Dutch | Zero-Resource Transfer From CoNLL-2003 English dataset. | F1 score | 74.61 | #1 of 1 | Archive leaderboard | report |
| Low Resource Named Entity Recognition | CONLL 2003 German | Zero-Resource Transfer From CoNLL-2003 English dataset. | F1 score | 65.24 | #1 of 1 | Archive leaderboard | report |
| Low Resource Named Entity Recognition | Conll 2003 Spanish | Zero-Resource Cross-lingual Transfer From CoNLL-2003 English dataset. | F1 score | 75.93 | #1 of 1 | Archive leaderboard | report |
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