Keras data augmentation with large dataset

#Keras data augmentation with large dataset Offline

In addition to question answering, Natural Language Processing (NLP) researchers are also exploring the application of abstractive summarization in which a model outputs a novel summary from a collection of input documents. Fitting 124 question-answer annotations without overfitting is extremely challenging in the current state of Deep Learning. The authors of the paper describe working for 23 hours to produce 124 question-answer pairs. constructed COVID-QA, a supervised learning dataset in which articles are annotated with an answer span to a given question. For example, one of the key Deep Learning applications for COVID-19 rapid response was question answering. The problem is that collecting these annotated datasets is very difficult at the scale required. Deep Learning can achieve remarkable performance through the combination of this learning strategy and large labeled datasets.

Supervised learning describes the use of loss functions that align predictions with manually annotated ground truth. Nearly all the successes of Deep Learning stem from supervised learning. We hope this paper inspires further research interest in Text Data Augmentation. 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).

#Keras data augmentation with large dataset Offline

For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. We highlight the key differences and promising ideas that have yet to be tested in NLP.

NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight studies that cover how augmentations can construct test sets for generalization. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data.

We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. In this survey, we consider how the Data Augmentation training strategy can aid in its development. Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning.