Papers with generative data augmentation methodology

1 papers
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions (2024.acl-long)

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Challenge: ABEX is a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks.
Approach: They propose a novel generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks based on a paradigm for generating diverse forms of an input document .
Outcome: The proposed method outperforms all baselines qualitatively with improvements of 0.04% - 38.8%.

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