Papers with bottom-k sampling

1 papers
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation (2023.eacl-main)

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Challenge: Curriculum Data Augmentation (CDA) presents synthetic data with increasing difficulties to neural models.
Approach: They propose a curriculum-aware paraphrase generation module with bottom-k sampling and cyclic learning strategy that passes through the curriculums multiple times.
Outcome: The proposed framework surpasses competitive baselines on few-shot text classification and dialogue generation.

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