Finally, we discuss interesting topics around Data Augmentation in NLP such as task-specific augmentations, the use of prior knowledge in self-supervised learning versus Data Augmentation, intersections with transfer and multi-task learning, and ideas for AI-GAs (AI-Generating Algorithms). We hope ...
Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundarie...
Back‑translation augmentation: 从一个语言翻译到另一个语言作为数据增强。 Style augmentation:一种利用深度网络来增强数据以训练其他深度网络的增强策略。这是一种有趣的策略,可以防止过度拟合高频特征或模糊语言形式,例如专注于意义。在文本数据域中,这可以描述将一位作者的写作风格转移到另一位作者的写作风格,以用...
Deep learningThis paper presents our work on using part-of-speech focused lexical substitution for data augmentation (PLSDA) to enhance the prediction capabilities and the performance of deep learning models. This paper explains how PLSDA uses part-of-speech information to identify words and make ...
To build useful Deep Learning models, the validation error must continue to decrease with the training error. Data Augmentation is a very powerful method of achieving this. The augmented data will represent a more comprehensive set of possible data points, thus minimizing the distance between the ...
However, the bottleneck for multimodal deep learning is the need for a large volume of multimodal training examples. Data augmentation techniques such as cropping, flipping, rotation, etc. are often employed in the image domain to improve the generalization of deep learning models. Augmenting in ...
Data Augmentation,使用CTG可以把已有的文本的某些信息给重新生成,变成我们想要的属性 Debiasing,这也非常重要,可以帮助我们把带有某些偏见的文本转化成无偏见的文本,让机器也符合伦理道德 Format Control,风格、格式的转换,比如中国古诗词就有明确的格式,这就需要在生成的时候加以控制 ...
论文由美国Protago实验室发表于 EMNLP-IJCNLP 2019 会议(short paper) 论文地址:[1901.11196] EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (arxiv.org) 代…
Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that ca
Data augmentation: Group内包含job title数量与group数量之间的分布:含有上百条title的group是很少的,通常一个group只含几个title 数据集采样:4:1的采样方式 4为between-class (negative) pairs 1为within-class (positive) pairs 分类数据集的建设:分为四个stage,每个stage又分为两步:1使用特殊属性来进行数据扩充...