# Zero-Resource Cross-Lingual Named Entity Recognition

arXiv: https://arxiv.org/abs/1911.09812 · HTML: https://mlsanity.com/paper/1911.09812/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 18 of 18)

- [ALLaM: Large Language Models for Arabic and English](https://mlsanity.com/paper/2407.15390/) (2024)
- [How to Adapt Your Pretrained Multilingual Model to 1600 Languages](https://mlsanity.com/paper/2106.02124/) (2021)
- [AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER](https://mlsanity.com/paper/2106.02300/) (2021)
- [CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation](https://mlsanity.com/paper/2210.07022/) (2022)
- [Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2106.00241/) (2021)
- [ConNER: Consistency Training for Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2211.09394/) (2022)
- [X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering](https://mlsanity.com/paper/2104.09696/) (2021)
- [Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models](https://mlsanity.com/paper/2210.13029/) (2022)
- [Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment](https://mlsanity.com/paper/2101.11112/) (2021)
- [Nearest Neighbour Few-Shot Learning for Cross-lingual Classification](https://mlsanity.com/paper/2109.02221/) (2021)
- [Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection](https://mlsanity.com/paper/2206.00962/) (2022)
- [Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition](https://mlsanity.com/paper/2107.10523/) (2021)
- [On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey](https://mlsanity.com/paper/2408.04879/) (2024)
- [Extending Isolation Forest for Anomaly Detection in Big Data via K-Means](https://mlsanity.com/paper/2104.13190/) (2021)
- [A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2204.00796/) (2022)
- [Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages](https://mlsanity.com/paper/2207.06882/) (2022)
- [Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages](https://mlsanity.com/paper/2305.14949/) (2023)
- [CodeNER: Code Prompting for Named Entity Recognition](https://mlsanity.com/paper/2507.20423/) (2025)

## Abstract (arXiv)

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.