Cross-Lingual NER
23 papers with code • 28 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Cross-Lingual NER
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Datasets
CoNLL 2003
WikiNEuRal
Most implemented papers
ByT5: Towards a token-free future with pre-trained byte-to-byte models
Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units.
Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
ikergarcia1996/Easy-Translate
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Zero-resource cross-lingual transfer approaches aim to apply supervised models from a source language to unlabelled target languages.
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.
Rethinking embedding coupling in pre-trained language models
PaddlePaddle/PaddleNLP
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ICLR 2021
We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models.
T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks
ikergarcia1996/t-projection
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In the absence of readily available labeled data for a given sequence labeling task and language, annotation projection has been proposed as one of the possible strategies to automatically generate annotated data.
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.
