计算机科学
帧(网络)
人工智能
多样性(政治)
稀缺
机器学习
自然语言
采样(信号处理)
自然语言处理
深度学习
数据科学
人类学
社会学
滤波器(信号处理)
经济
微观经济学
电信
计算机视觉
作者
Bohan Li,Yutai Hou,Wanxiang Che
标识
DOI:10.1016/j.aiopen.2022.03.001
摘要
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in many tasks. One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping the model to better generalize to unseen testing data. In this survey, we frame DA methods into three categories based on the diversity of augmented data, including paraphrasing, noising, and sampling. Our paper sets out to analyze DA methods in detail according to the above categories. Further, we also introduce their applications in NLP tasks as well as the challenges. Some helpful resources are provided in the appendix.
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