Papers with order-agnostic data augmentation

1 papers
Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition (2024.acl-long)

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Challenge: Existing DA methods for named entity recognition (NER) are costly and labor-intensive to acquire, necessitating innovative approaches to data scarcity.
Approach: They propose an order-agnostic data augmentation solution that exploits the order-based property in the training phase of sequence-to-sequence NER methods for data augmented.
Outcome: The proposed method significantly enhances the few-shot capabilities of pre-trained language models in low-resource settings.

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