Can Ready-to-Use RNNs Generate “Good” Text Training Data?

Abstract

There is much research on the state-of-the-art techniques for generating training data through neural networks. However, many of these techniques are not easily implemented or available due to factors such as copyright of their research code. Meanwhile, there are other neural network codes currently available that are easily accessible for individuals to generate text data; this paper explores the quality of the text data generated by these ready-to-use neural networks for classification tasks. This paper’s experiment showed that using the text data generated by a default configured RNN to train a classification model can match closely with baseline accuracy.