Cross-Lingual NER
11 papers with code • 8 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Cross-Lingual NER
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WikiAnn
WikiNEuRal
Most implemented papers
ByT5: Towards a token-free future with pre-trained byte-to-byte models
In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences.
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
shijie-wu/crosslingual-nlp
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IJCNLP 2019
Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks.
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
thespectrewithin/joint-align
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ICLR 2020
Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks.
Multi-Source Cross-Lingual Model Transfer: Learning What to Share
microsoft/Multilingual-Model-Transfer
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ACL 2019
In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance.
Entity Projection via Machine Translation for Cross-Lingual NER
Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition.
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).
Zero-Resource Cross-Lingual Named Entity Recognition
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.
Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language
microsoft/vert-papers
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ACL 2020
However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language.
UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data
Prior works in cross-lingual named entity recognition (NER) with no/little labeled data fall into two primary categories: model transfer based and data transfer based methods.
Semi-Supervised Disentangled Framework for Transferable Named Entity Recognition
In the proposed framework, the domain-specific information is integrated with the domain-specific latent variables by using a domain predictor.
