论文标题
PCC：通过底部K采样和循环学习的隔离，以增加课程数据
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation
论文作者
论文摘要
课程数据增强（CDA）通过呈现综合数据，从而使难度从易于努力提高到综合数据来改善神经模型。但是，传统CDA只是将单词扰动的比率视为难度度量，而仅通过一次课程。本文介绍了\ textbf {pcc}：\ textbf {p} araphrasing in powts-k采样和\ textbf {c} yclic学习\ textbf {c} urriculum数据增强，这是一种通过paraphrasing进行的新颖的CDA框架，通过paraphrasing进行了paraphrasing，该范围可衡量textual paraphrane sextual paraph sy confricul s an Crricul in Corrcricul king the Crorricul s an Crricul the Crorricul。我们提出了一个由三个单元组成的课程释义生成模块：具有底部K采样的释义候选者，滤波机制和难度度量。我们还提出了一种循环学习策略，该策略多次通过课程。提出了底部K采样以生成后来课程的超硬实例。几乎没有的文本分类以及对话生成的实验结果表明，PCC超过了竞争基线。人类评估和广泛的案例研究表明，底部K采样有效地产生了超硬的实例，PCC显着改善了基线对话代理。
Curriculum Data Augmentation (CDA) improves neural models by presenting synthetic data with increasing difficulties from easy to hard. However, traditional CDA simply treats the ratio of word perturbation as the difficulty measure and goes through the curriculums only once. This paper presents \textbf{PCC}: \textbf{P}araphrasing with Bottom-k Sampling and \textbf{C}yclic Learning for \textbf{C}urriculum Data Augmentation, a novel CDA framework via paraphrasing, which exploits the textual paraphrase similarity as the curriculum difficulty measure. We propose a curriculum-aware paraphrase generation module composed of three units: a paraphrase candidate generator with bottom-k sampling, a filtering mechanism and a difficulty measure. We also propose a cyclic learning strategy that passes through the curriculums multiple times. The bottom-k sampling is proposed to generate super-hard instances for the later curriculums. Experimental results on few-shot text classification as well as dialogue generation indicate that PCC surpasses competitive baselines. Human evaluation and extensive case studies indicate that bottom-k sampling effectively generates super-hard instances, and PCC significantly improves the baseline dialogue agent.
