Papers with model-collaboration-based denoising scheme

1 papers
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition (2023.acl-long)

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Challenge: Existing approaches to named entity recognition (NER) are limited by the cost of labeling and labeling, especially for low-resource languages.
Approach: They propose a model-collaboration-based denoising scheme that enables models trained on different data sources to collaboratively denoise pseudo labels used by each other.
Outcome: The proposed framework achieves superior results on benchmark datasets and can generalize to distant languages.

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