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Curriculum Data Augmentation (CDA) improves neural models by presentingsynthetic data with increasing difficulties from easy to hard. However,traditional CDA simply treats the ratio of word perturbation as the difficultymeasure and goes through the curriculums only once. This paper presents\textbf{PCC}: \textbf{P}araphrasing with Bottom-k Sampling and \textbf{C}yclicLearning for \textbf{C}urriculum Data Augmentation, a novel CDA framework viaparaphrasing, which exploits the textual paraphrase similarity as thecurriculum difficulty measure. We propose a curriculum-aware paraphrasegeneration module composed of three units: a paraphrase candidate generatorwith bottom-k sampling, a filtering mechanism and a difficulty measure. We alsopropose a cyclic learning strategy that passes through the curriculums multipletimes. The bottom-k sampling is proposed to generate super-hard instances forthe later curriculums. Experimental results on few-shot text classification aswell as dialogue generation indicate that PCC surpasses competitive baselines.Human evaluation and extensive case studies indicate that bottom-k samplingeffectively generates super-hard instances, and PCC significantly improves thebaseline dialogue agent.

PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation