# CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation

arXiv: https://arxiv.org/abs/2210.07022 · HTML: https://mlsanity.com/paper/2210.07022/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 49 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs](https://mlsanity.com/paper/1911.06154/) (2019)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Contrastive Learning for Many-to-many Multilingual Neural Machine Translation](https://mlsanity.com/paper/2105.09501/) (2021)
- [Offline bilingual word vectors, orthogonal transformations and the inverted softmax](https://mlsanity.com/paper/1702.03859/) (2017)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://mlsanity.com/paper/2004.14723/) (2020)
- [Neural Cross-Lingual Named Entity Recognition with Minimal Resources](https://mlsanity.com/paper/1808.09861/) (2018)
- [Code-switching pre-training for neural machine translation](https://mlsanity.com/paper/2009.08088/) (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)
- [RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models](https://mlsanity.com/paper/2109.05620/) (2021)
- [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)
- [Importance-based Neuron Allocation for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2107.06569/) (2021)
- [Zero-Resource Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/1911.09812/) (2019)
- [Distributionally Robust Multilingual Machine Translation](https://mlsanity.com/paper/2109.04020/) (2021)
- [Discontinuous Named Entity Recognition as Maximal Clique Discovery](https://mlsanity.com/paper/2106.00218/) (2021)
- [Unsupervised Pivot Translation for Distant Languages](https://mlsanity.com/paper/1906.02461/) (2019)
- [Sources of Transfer in Multilingual Named Entity Recognition](https://mlsanity.com/paper/2005.00847/) (2020)
- [Competence-based Curriculum Learning for Multilingual Machine Translation](https://mlsanity.com/paper/2109.04002/) (2021)
- [Boosting Question Answering by Deep Entity Recognition](https://mlsanity.com/paper/1605.08675/) (2016)

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

- [xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning](https://mlsanity.com/paper/2401.07037/) (2024)
- [C-ICL: Contrastive In-context Learning for Information Extraction](https://mlsanity.com/paper/2402.11254/) (2024)
- [GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator](https://mlsanity.com/paper/2212.10218/) (2022)
- [mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning](https://mlsanity.com/paper/2308.09073/) (2023)
- [Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies](https://mlsanity.com/paper/2402.09282/) (2024)
- [New Intent Discovery with Attracting and Dispersing Prototype](https://mlsanity.com/paper/2403.16913/) (2024)
- [SVIPTR: Fast and Efficient Scene Text Recognition with Vision Permutable Extractor](https://mlsanity.com/paper/2401.10110/) (2024)
- [XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser](https://mlsanity.com/paper/2405.17336/) (2024)
- [MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction](https://mlsanity.com/paper/2308.06552/) (2023)
- [RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations](https://mlsanity.com/paper/2404.08977/) (2024)
- [Improving Pseudo Labels with Global-Local Denoising Framework for Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2406.01213/) (2024)
- [Towards Real-world Scenario: Imbalanced New Intent Discovery](https://mlsanity.com/paper/2406.03127/) (2024)
- [CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2305.14913/) (2023)
- [A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala](https://mlsanity.com/paper/2412.02056/) (2024)
- [Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource Languages](https://mlsanity.com/paper/2501.18750/) (2025)
- [LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction](https://mlsanity.com/paper/2607.23420/) (2026)
- [LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction](https://mlsanity.com/paper/2606.29407/) (2026)

## Abstract (arXiv)

Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. Cross-lingual NER has been proposed to alleviate this issue by transferring knowledge from high-resource languages to low-resource languages via aligned cross-lingual representations or machine translation results. However, the performance of cross-lingual NER methods is severely affected by the unsatisfactory quality of translation or label projection. To address these problems, we propose a Cross-lingual Entity Projection framework (CROP) to enable zero-shot cross-lingual NER with the help of a multilingual labeled sequence translation model. Specifically, the target sequence is first translated into the source language and then tagged by a source NER model. We further adopt a labeled sequence translation model to project the tagged sequence back to the target language and label the target raw sentence. Ultimately, the whole pipeline is integrated into an end-to-end model by the way of self-training. Experimental results on two benchmarks demonstrate that our method substantially outperforms the previous strong baseline by a large margin of +3~7 F1 scores and achieves state-of-the-art performance.