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Research map / Label-efficient and robust learning / Recognition and long-tail

Enhanced Meta-Learning for Cross-Lingual Named Entity Recognition with Minimal Resources

AAAI 2020 · Label-efficient and robust learning › Recognition and long-tail · approach: Multilingual language representation pretraining · 72 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use enhanced meta-learning with retrieved similar source examples for per-example adaptationmBERT cross-lingual NER · Cross-lingual NER average (five target languages)F1: 70.45 -> 72.71Helped
Add the maximum-loss term during meta-training (ablation)Enhanced meta-learning for cross-lingual NER · Cross-lingual NER average (five target languages)F1: 71.36 -> 72.71; +1.35Helped
Add entity masking during meta-training (ablation)Enhanced meta-learning for cross-lingual NER · CoNLL-2002 Spanish testbF1: 75.57 -> 76.75; +1.18Helped
Add both max loss and entity masking (ablation)Meta-learning cross-lingual NER · Cross-lingual NER average (five target languages)F1: 71.25 -> 72.71; +1.46Helped
Add meta-training and per-example adaptation (ablation)mBERT cross-lingual NER · Europeana Newspapers French testF1: 50.89 -> 55.30; +4.41Helped
Use the augmented max-loss function during adaptation instead of the standard lossAdaptation phase of the proposed method · Cross-lingual NERF1Mixed

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