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

arXiv: https://arxiv.org/abs/2106.02300 · HTML: https://mlsanity.com/paper/2106.02300/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [Revisiting Few-sample BERT Fine-tuning](https://mlsanity.com/paper/2006.05987/) (2020)
- [Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification](https://mlsanity.com/paper/1606.01614/) (2016)
- [Neural Cross-Lingual Named Entity Recognition with Minimal Resources](https://mlsanity.com/paper/1808.09861/) (2018)
- [XtremeDistil: Multi-stage Distillation for Massive Multilingual Models](https://mlsanity.com/paper/2004.05686/) (2020)
- [Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection](https://mlsanity.com/paper/1707.02483/) (2017)
- [Entity Projection via Machine Translation for Cross-Lingual NER](https://mlsanity.com/paper/1909.05356/) (2019)
- [Acquiring Knowledge from Pre-trained Model to Neural Machine Translation](https://mlsanity.com/paper/1912.01774/) (2019)
- [Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing](https://mlsanity.com/paper/2004.00280/) (2020)
- [Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER](https://mlsanity.com/paper/1909.00153/) (2019)
- [Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources](https://mlsanity.com/paper/1911.06161/) (2019)
- [UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data](https://mlsanity.com/paper/2007.07683/) (2020)
- [GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition](https://mlsanity.com/paper/1907.05611/) (2019)
- [Zero-Resource Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/1911.09812/) (2019)
- [Towards Oracle Knowledge Distillation with Neural Architecture Search](https://mlsanity.com/paper/1911.13019/) (2019)

## Built on by (most cited first, 17 of 17)

- [CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning](https://mlsanity.com/paper/2305.04808/) (2023)
- [mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning](https://mlsanity.com/paper/2308.09073/) (2023)
- [ConNER: Consistency Training for Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2211.09394/) (2022)
- [A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends](https://mlsanity.com/paper/2302.03512/) (2023)
- [Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning](https://mlsanity.com/paper/2305.13628/) (2023)
- [WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2212.03506/) (2022)
- [Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts](https://mlsanity.com/paper/2204.04775/) (2022)
- [A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2204.00796/) (2022)
- [ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2301.08855/) (2023)
- [CMNER: A Chinese Multimodal NER Dataset based on Social Media](https://mlsanity.com/paper/2402.13693/) (2024)
- [Improving Pseudo Labels with Global-Local Denoising Framework for Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2406.01213/) (2024)
- [SHINE: Syntax-augmented Hierarchical Interactive Encoder for Zero-shot Cross-lingual Information Extraction](https://mlsanity.com/paper/2305.12389/) (2023)
- [CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2305.14913/) (2023)
- [Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages](https://mlsanity.com/paper/2305.14949/) (2023)
- [DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning](https://mlsanity.com/paper/2403.04158/) (2024)
- [Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective](https://mlsanity.com/paper/2509.01147/) (2025)
- [A Mechanism and Optimization Study on the Impact of Information Density on User-Generated Content Named Entity Recognition](https://mlsanity.com/paper/2604.18944/) (2026)

## Abstract (arXiv)

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