| Challenge: | Named-entity recognition (NER) models are highly dependent on large amounts of labeled data. |
| Approach: | They propose a method that finds translations based on bilingual word embeddings . they also propose 'self-attention' which allows for a degree of flexibility with respect to word order . |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance on common languages with lower resource requirements than previous approaches. |
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What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (D19-1)
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| Challenge: | Named entity recognition models are challenging for languages with little training data. |
| Approach: | They propose a simple and efficient neural architecture for cross-lingual named entity recognition models. |
| Outcome: | The proposed model achieves competitive performance with the state-of-the-art on two transferable factors: sequential order and multilingual embedding. |
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers (D19-1)
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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
| Approach: | They propose a method for bootstrapping named entity recognition models in under-resourced languages . they use cross-lingual transfer learning and targeted annotation of only uncertain entities . |
| Outcome: | The proposed method achieves competitive accuracy with just one-tenth of training data. |
Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing studies focus on internal features of Chinese named entity recognition, but neglect other lingual modalities. |
| Approach: | They propose a bilingual enhancement module for Chinese Named Entity Recognition . they integrate rich English information into Chinese representation and use it to learn the interaction between bilinguals and dependent information within Chinese. |
| Outcome: | The proposed model can learn the interaction of bilinguals and dependent information within Chinese. |
Constrained Labeled Data Generation for Low-Resource Named Entity Recognition (2021.findings-acl)
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| Challenge: | Named Entity Recognition (NER) in lowresource languages has been a challenge for years . Existing methods suffer from low quality of annotated data in target language . |
| Approach: | They propose a method that uses projected annotations to generate pseudo supervised data with a transformer language model and a constrained beam search. |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance in low-resource languages. |
Entity Projection via Machine Translation for Cross-Lingual NER (D19-1)
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| Challenge: | a subset of languages have large annotated corpora for named entity recognition. |
| Approach: | They propose a system that leverages machine translation systems twice to improve named entity recognition. |
| Outcome: | The proposed system outperforms existing methods on Armenian languages by 4.1 points . it achieves state-of-the-art F_1 scores for Armenian, outperforming monolingual model trained on Armenia. |
Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective (2025.findings-emnlp)
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| Challenge: | Existing approaches to cross-lingual Named Entity Recognition focus on Latin script language (LSL) for non-Latin script language, performance often degrades due to deep structural differences. |
| Approach: | They propose an entity-aligned translation approach to align entities between NSL and English . |
| Outcome: | The proposed approach aims to transfer knowledge from high-resource languages to low-resourced languages. |
Data Augmentation for Cross-Domain Named Entity Recognition (2021.emnlp-main)
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| Challenge: | Existing methods for named entity recognition focus on augmenting in-domain data in low-resource scenarios where annotated data is limited. |
| Approach: | They propose a neural architecture to transform data from high-resource to low-resourced domains by learning the patterns in the text that differentiate them. |
| Outcome: | The proposed approach improves on high-resource domain representations over high- and low-resourced domains. |
Improving Low-Resource Languages in Pre-Trained Multilingual Language Models (2022.emnlp-main)
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| Challenge: | Pre-trained multilingual language models are the foundation of many NLP approaches, but are often not well-supported by these models due to small available monolingual corpora. |
| Approach: | They propose an unsupervised approach to improve cross-lingual representations of low-resource languages by bootstrapping word translation pairs from monolingual corpora and using them to improve language alignment. |
| Outcome: | The proposed approach improves cross-lingual representations on low-resource languages using word retrieval and zero-shot named entity recognition. |
Sources of Transfer in Multilingual Named Entity Recognition (2020.acl-main)
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| Challenge: | naive training of named-entity recognition models using annotated data from multiple languages consistently underperforms monolingual models. |
| Approach: | They propose a polyglot named-entity recognition model where one model is trained using annotated data drawn from multiple languages. |
| Outcome: | The proposed model outperforms models trained on monolingual data despite more training data . the proposed model shares many parameters across languages and fine-tunes them to outperFORM monolingual models. |
CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation (2022.findings-emnlp)
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Jian Yang, Shaohan Huang, Shuming Ma, Yuwei Yin, Li Dong, Dongdong Zhang, Hongcheng Guo, Zhoujun Li, Furu Wei
| Challenge: | Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. |
| Approach: | They propose a cross-lingual entity projection framework to enable zero-shot cross-linguistic NER with the help of a multilingual labeled sequence translation model. |
| Outcome: | The proposed method outperforms the baseline method on two benchmarks by a large margin of +3 7 F1 scores and achieves state-of-the-art performance. |